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12 Data Scientist CV Examples - Here's What Works In 2024

Simply put, this piece lays out some prime examples of data scientist cvs that landed the job. as a data scientist hopeful, you'll pick up key skills to make your cv shine. the cv is your opening pitch. make it count. lay out your prowess in data mining, algorithm creation, and machine learning in a way that grabs attention..

As a data scientist, you take complex info and turn it into wisdom that speaks. This cogent trade is all about taking messy data, stewing it in analytical powers, and serving stats that really mean something. Your CV should do the same. It must mirror your mastery at summarizing vast data into a neat, easy-to-read snapshot of your career path and skills. It should be your golden ticket that rides you through the scramble of the hiring process. Data scientists have quite a journey. Often, you cut your teeth coding, learn to navigate the sea of data with savvy analytic skills, then, like icing on a cake, you frost your tech knowledge with a strong knack for business sense. As a hiring manager, we want to see this career progression clearly outlined in your CV. Next, we get into trends. Fast forward to 2024, and we've seen the rise of "storytelling with data". This isn't a mere buzzword folks, but a key skill we now scout for. The ability to take complex data and tell a clear story that engages even those less versed in data. Now, let's clear the fog around CVs and resumes. In the data science field, you may see the terms "CV" and "Resume" floating around. It might seem like a puzzle, but it's as simple as this: they're the same. No matter if you're in the UK, Europe, or Australia, these are just different words for the same thing. You might hear a CV is a longer 3-4 page document. But that's only for roles in research or academia, where your story includes many publications. For your run-of-the-mill data science role, we're talking a neat 1-2 page document. In the rest of this article, we'll dive into CV building blocks for data scientists. We'll storm through hot-off-the-press 2024 templates, explore which skills to highlight and how, and more. Stay tuned. We'll guide your CV to speak volumes with just a skim.

Data Scientist CV Templates

Jump to a template:

• Data Scientist
• Senior Data Scientist
• Entry Level Data Scientist
• Data Science Manager
• Data Science Vice President
• Junior Data Scientist
• Career Change into Data Science

Jump to a resource:

• Keywords for Data Scientist CVs
• Action Verbs to Use
• Bullet Points on Data Scientist CVs
• Frequently Asked Questions
• Related Data & Analytics CVs
• Similar Careers to a Data Scientist
• Data Scientist Resume Examples

Template 1 of 12: Data Scientist CV Example

Data Scientist is a role that involves diving into heaps of raw data, mining useful information, and using it to make informed decisions. It's like being a detective, but for numbers. It's fascinating how machine learning and artificial intelligence are revolutionizing this field, with many companies using these technologies to predict trends and behaviors. When crafting your CV, remember that your potential employer wants to see a blend of technical proficiency, a keen analytical mind, and strong communication skills. As the saying goes, "data is the new oil", and companies are on the hunt for professionals who can help them refine it. In recent times, there's been a shift towards utilizing complex models like neural networks, emphasizing both the need for advanced skills and a deep understanding of business needs. This isn't just about number-crunching - it's about extracting meaningful insights that shape a company's strategy.

Tips to help you write your Data Scientist CV in 2024

   showcase your experience with ml/ai.

As a data scientist, your understanding and experience with machine learning and artificial intelligence are crucial. On your CV, enumerate specific projects you've done that involved these technologies, delve into the methodologies you used, and the impact of the project on the organization.

   Demonstrate your business acumen

Being technically sound isn't enough - you also need to demonstrate your ability to translate complex data into business insights. Use your CV to highlight specific instances where your data analysis led to a strategic decision or helped solve a business problem.

Skills you can include on your Data Scientist CV

Template 2 of 12: data scientist cv example.

Data Science is a rapidly evolving field, with companies across every industry seeking experts who can harness and interpret data. A CV for a data scientist needs to capture your ability to use complex mathematical techniques to solve business problems. It should reflect your grasp of data mining and machine learning, as well as your proficiency in coding languages. Additionally, it should showcase your ability to communicate complex ideas effectively because, as a data scientist, you'll often need to explain your findings to non-technical colleagues. A strong data scientist CV presents an ever-green passion for learning, given the rapid changes in data science techniques. Additionally, it's vital to demonstrate your understanding of ethical considerations in data science, as there's growing concern about privacy and misinformation.

   Showcase your tackling of real-world problems

In your CV, make sure to include specific examples of how you have successfully applied your data science skills to solve real-world problems. This means providing details on the project, the challenges faced, the data science techniques employed, and the impact created. These examples better illustrate your capabilities and achievements.

   Demonstrate your coding proficiency

You should list down the coding languages you're proficient in, along with any data processing tools you've utilized in your work. Moreover, it's beneficial to provide a link to your GitHub or other code repositories so hiring managers can see your actual work, your coding style and how you handle complex data tasks.

Template 3 of 12: Senior Data Scientist CV Example

As a Senior Data Scientist, you're pivotal in creating data-driven strategies in business. The role's spotlight has grown from just number crunching or coding to leveraging data insights for major business decisions. Nowadays, more firms are leaning towards Senior Data Scientists who marry data analysis with effective communication skills - it's not just about the numbers anymore, but how you interpret and present them to team members or stakeholders. When writing your CV, remember, you're not just showcasing your technical proficiency, but also your capacity to use data to influence business strategies. Companies are on a constant lookout for experienced data scientists who can independently navigate through business problems. Having a CV that reflects your knack to take data insights beyond the technical team to non-technical stakeholders will set you apart. So, while you're detailing your data modeling techniques or AI-based project experience, don't forget to demonstrate your communication skills and business acumen.

Tips to help you write your Senior Data Scientist CV in 2024

   showcase end-to-end project ownership.

As a senior figure, taking ownership of projects is vital. Detail in your CV how you've led data science projects from the formulation of the problem statement, data gathering, model development, to deploying the solution and maintaining it post-deployment.

   Stress on your business acumen

Companies look for Senior Data Scientists who can interpret data and translate it into strategic business decisions. In your CV, elaborate on your experience in advising business leaders or stakeholders based on data analysis, shedding light particularly on the tangible impacts created.

Skills you can include on your Senior Data Scientist CV

Template 4 of 12: senior data scientist cv example.

As a hiring manager, I find many senior data scientist applicants miss the mark when crafting their CVs. This role is unique, bridging the gap between the technical world of data and the strategic side of business. Lately, companies are leaning towards candidates who not only have technical proficiency, but also possess a strong understanding of business operations. When drafting your CV, it's crucial to demonstrate how you've used your data skills to drive business outcomes. In this rapidly-changing field, it's equally essential to showcase your ability to stay abreast of emerging tools and techniques.

   Emphasize end-to-end project management

Given the strategic nature of senior data scientist roles, hiring managers value candidates who have experience managing projects from initiation to completion. On your CV, detail how you've led complex data projects, outlining the specific steps you took and the results achieved.

   Illustrate adaptability to new technology

The field of data science is continually evolving, with new technologies and algorithms emerging regularly. Therefore, ensure your CV reflects your ability to learn and adapt to new tools quickly. Include instances where you've had to quickly master new technologies or methodologies to deliver solutions.

Template 5 of 12: Entry Level Data Scientist CV Example

Jumpstarting your career in data science requires a well-tailored CV for entry-level positions. This booming field is all about unleashing the potential of data to drive strategic decisions, with a knack for statistical analysis, programming, and a curious mindset. Aspiring data scientists are flooding the market, so your CV should reflect current industry requirements, emphasizing your technical skills, project experiences, and understanding of data-driven decision-making. Now, remember, companies nowadays are not just looking for number-crunchers, they're seeking team players with strong communication skills who can translate complex data insights into comprehensible narratives for non-technical stakeholders.

Tips to help you write your Entry Level Data Scientist CV in 2024

   showcase your relevant technical skills.

Companies are seeking entry-level data scientists who are proficient in the tools and programming languages integral to the role. Show off your skills with Python, R, SQL, or any other relevant software. Make sure to include your proficiency in data visualization tools like Tableau and Power BI.

   Highlight your practical experience

Getting your feet wet in real-world scenarios is crucial in this field. Detail any pertinent internships, co-op placements, or coursework where you applied your data science skills. Even if it's an unpaid project or university assignment, if it showcases your ability to analyze and interpret data, it's worth including.

Skills you can include on your Entry Level Data Scientist CV

Template 6 of 12: entry level data scientist cv example.

As an Entry Level Data Scientist, you'll be immersing yourself in data, learning how to turn it into actionable insights. There's a recent trend for companies to aim for data-driven decision-making, making this an in-demand role. When crafting your CV, it's fundamental to remember that, as an entry level position, recruiters will focus more on your potential than your experience. Therefore, showcasing your learning agility, curiosity, and passion for data science is as important as demonstrating your technical skills. Furthermore, many recruiters use automated systems to filter out CVs, so ensure that yours is filled with relevant keywords and industry-specific jargon.

   Include relevant academic projects

While you may not have much professional experience, you can demonstrate your abilities by detailing relevant academic projects you've completed. Describe the project, what data you used, the tools you employed, and the results achieved.

   Show proficiency in data science tools

Recruiters are interested in your ability to use data science tools like Python, R, or SAS. Make sure you list the tools you're familiar with and provide examples of how you've used them in projects or coursework.

Template 7 of 12: Data Science Manager CV Example

An effective CV for a Data Science Manager role must reflect a unique blend of technical expertise, managerial acumen, and recent industry trends. In a rapidly evolving field like data science, it's essential that your CV communicates not only hands-on experience with data analytics and machine learning but also a clear understanding of the business impact of your work. Recent trends show that companies prioritize data science managers who can interpret complex data and translate it into business strategies. On another note, being a manager in this field requires a certain level of leadership and communication skills. When writing your CV, it's crucial to illustrate that you're more than just a number-cruncher; you're a team leader who can guide and inspire your colleagues, all while maintaining a clear view of the broader organizational goals.

Tips to help you write your Data Science Manager CV in 2024

   showcase technical proficiency with business impact.

You should specifically note instances where you applied technical skills, like Python or SQL, to drive tangible business outcomes. Maybe you developed a predictive model that reduced operational costs, or used machine learning to enhance customer experience. These are the stories that get noticed.

   Highlight management and communication skills in data projects

As a Data Science Manager, your ability to manage people and projects is as important as your technical know-how. Detail your involvement in mentoring team members, liaising with other departments, or presenting data insights to non-technical stakeholders. This shows you're a well-rounded candidate who can operate effectively in a managerial role.

Skills you can include on your Data Science Manager CV

Template 8 of 12: data science manager cv example.

A data science manager's role is a unique blend of technical expertise and leadership. As a candidate, you must show a solid background in data science, combined with the ability to manage and lead a team. The trend of digital transformation is increasing the dependence of organizations on data-driven decision making, making this role more critical than ever. When drafting your CV, remember that companies don't just want a number whiz; they want a strategic thinker who can use data to drive business. They also want someone who can communicate complex data to non-technical stakeholders. As a data science manager, you're going to face the dual challenge of proving you can manage a team and handle data-driven decisions. Hence, your CV should also reflect how you can benefit the organization with your strategic insights and team management abilities. Just as the technology and data science tools evolve continually, so should your skills and knowledge. Keep your CV updated with your current technical skills, latest projects, and their impact on the business.

   Showcase your ability to translate data

As a Data Science Manager, you're the bridge between raw data and business strategies. Make sure your CV underlines your ability to interpret complex data and convert it into actionable insights. Provide examples of how you've influenced decisions or strategies using data in the past.

   Highlight experience in leading data science projects

Your CV should demonstrate your experience in managing data science projects from concept to execution. Detail your role in these projects, the methodologies you used, the team size you managed, and the business outcomes achieved.

Template 9 of 12: Data Science Vice President CV Example

With the surge in data-driven decision making, a Vice President of Data Science role is critical in linking technical data analysis to strategic business pursuits. The role involves steering the data science team while ensuring data insights are aligned with company goals. Your CV should reflect this ability to bridge tech and strategy. It's important to know that hiring managers increasingly value industry-specific knowledge, coupled with strong leadership skills. Tailoring your CV to showcase not just your technical expertise but also your capacity to make data-driven decisions in line with industry trends and company objectives can set you apart.

Tips to help you write your Data Science Vice President CV in 2024

   showcase your cross-disciplinary expertise.

VPs of Data Science are expected to have a solid understanding of several areas: data science, business strategy, and industry trends. Your CV should detail your technical skills, such as expertise in machine learning or predictive modeling, as well as your understanding of the business landscape and the ability to draw insights from data that propel the company forward.

   Emphasize leadership and communication skills

Unlike entry-level data science roles, a Vice President position requires strong leadership and communication skills. You should include examples of how you’ve led a data science team, set the strategy, and communicated complex data insights effectively to non-technical stakeholders.

Skills you can include on your Data Science Vice President CV

Template 10 of 12: data science vice president cv example.

As a Data Science Vice President candidate, you'll be expected to demonstrate a commanding grasp of both technology and management. With the recent boom in AI, machine learning, and big data analytics, companies are on the hunt for leaders who can not only understand these complex fields, but also translate this understanding to other c-suite members and teams. In your CV, you'll need to mix hard technical skills with soft leadership ones - ideally, showing how you've used both to drive real results. A CV for a Data Science VP role is more than a chronology - it's a demonstration of your ability to envision, strategize and deliver value from data. Companies want to see you've got the technical chops, sure, but it's just as important to show that you can manage teams, guide technology decisions and work with other senior staff to make the company more data-driven.

   Showcase strategic data-related achievements

Companies hiring for a Data Science VP want leaders who can strategize and deliver big-picture results. Use real-world examples of how you've leveraged data to drive growth, optimize processes, or solve complex problems to demonstrate this.

   Detail leadership in technical teams

For a position like this, you need to illustrate your ability to lead teams in technical environments. Include details of your experience in managing data scientists, ML engineers, or data analysts and the outcomes of the projects under your leadership.

Template 11 of 12: Junior Data Scientist CV Example

Writing your CV for a Junior Data Scientist role requires a sharp focus on demonstrating your data-related skills and proficiency in data analysis tools. A shift in the industry has seen an increased demand for data scientists with good programming skills, so make sure you emphasize yours. Companies are also looking for candidates capable of understanding complex data trends and crafting data-driven solutions. It's important not to overlook the significance of communication skills too; data scientists often present findings to non-technical staff, so you need to show you can translate data into clear and understandable insights. When crafting your CV, don’t just list your qualifications; demonstrate your passion for data analysis and problem-solving. A Junior Data Scientist role is a stepping stone to more advanced positions, so employers want to see your hunger for knowledge and your commitment to this route in your CV. In today’s data-driven business environment, companies are seeking data scientists who can provide innovative approaches and fresh perspectives, so try to highlight how you can bring this to their table.

Tips to help you write your Junior Data Scientist CV in 2024

   include a project portfolio.

In the 'Experience' section, include a project portfolio that showcases the breadth and depth of your data analysis skills. Mention the tools and programming languages you used, the problems you faced, and the solutions you implemented. The portfolio will display not just your technical skills, but also your creative problem-solving ability, which is crucial for a data scientist.

   Demonstrate your knowledge of data science tools

Be explicit about your proficiency in data science tools, especially Python, R, SQL, and Excel. Do not just mention them; provide context of where and how you have applied them. For example, talk about the projects where you used Python for data cleaning or SQL for database management. This will give employers proof of your hands-on experience and competence.

Skills you can include on your Junior Data Scientist CV

Template 12 of 12: career change into data science cv example.

Data science is an exciting field that offers substantial opportunity for growth and impact. If you're looking to transition into it, be aware that it's a field favored by industries hungry for insights from big data. In recent times, even sectors like retail and healthcare are embracing data science. This versatility means a data scientist CV should highlight a wide range of skills and knowledge. Reflecting your passion for mathematics, statistics, and programming is crucial. Equally important is your ability to draw insights from complex datasets and communicate them in a manner non-technical stakeholders can understand. In the wake of increasing remote work, many companies are also looking for data scientists who can work independently and collaboratively. You might be switching careers, but don't forget to leverage your previous experience to showcase these transferable skills. Remember, an effective CV for a data science role blends technical competency with an understanding of the industry's larger context.

Tips to help you write your Career Change into Data Science CV in 2024

Data science involves working with various tools. You need to demonstrate proficiency in programming languages like Python or R, as well as with data visualization tools such as Tableau or Power BI. Also mention familiarity with machine learning and big data platforms.

   Quantify your impact in previous roles

Even though you're making a career shift, the ability to drive results is critical. Don't just mention tasks from your previous job; quantify the impact you made. For instance, if you improved a process, mention the improvement percentage or time saved.

Skills you can include on your Career Change into Data Science CV

Skills for data scientist resumes.

In the numerical world of data science, skills speak louder than words. As a data scientist, your CV should scream statistical analysis, data management, and software fluency. Crunch the numbers, extract the insights, and mold these into a list of skills as clear as your data visualizations. With your skills section, imagine you're strategically mining the job description. Pick relevant skills and plant them as golden nuggets in both your Skills and Work Experience sections. For instance, "Spearheaded project using Python for predictive analysis." Want to decode the CV scanner and outwit the algorithm? Just remember, it's all about relevancy. When it sifts through CVs, it's searching for keyword matches from the job spec. So make sure your skillset fits the bill. In short, fill your CV with relevant skills and you're one step closer to clinching that data scientist role. Be sure to show, not just tell, how you've used these skills. So keep it simple, keep it factual, and let your skills do the talking.

• Machine Learning
• Deep Learning
• Apache Spark
• Software Development
• Predictive Analytics
• Data Science
• R (Programming Language)

Data Mining

• Statistical Modeling
• Artificial Intelligence (AI)
• Python (Programming Language)
• Predictive Modeling
• Natural Language Processing (NLP)

Skills Word Cloud For Data Scientist CVs

This word cloud highlights the important keywords that appear on Data Scientist job descriptions and CVs. The bigger the word, the more frequently it appears on job postings, and the more 'important' it is.

How to use these skills?

Action verbs for data scientist resumes.

A data scientist, a wizard of numbers, uses facts and logic to solve problems. Your CV should show how you mine data gold. Let's talk about action verbs. Words that show what you can do. Action verbs bring your CV to life. For instance, you might say, "Analyzed sales data from 15 stores to figure out which items sold best." See how "Analyzed" shows the way you work? Or you could write, "Developed machine learning models to predict customer behavior." There, "Developed" shows your creating side. Pick verbs that say a lot. Verbs like "optimized," "visualized," or "identified." These words capture how you do your job as a data scientist. So make your verbs work hard. Just like you do.

• Interpreted
• Experimented
• Streamlined
• Manipulated
• Implemented

For a full list of effective CV action verbs, visit Resume Action Verbs .

Resume Bullet Points From Data Scientist CV

You should use bullet points to describe your achievements in your Data Scientist CV. Here are sample bullet points to help you get started:

Developed web scraping program in Python to help the firm download public data, including over 10,000 company descriptions and stock quotes, enriching internal data and increasing research efficiency by over 50%

Produced comprehensive pitch decks, used for internal research and external marketing, on 1) an Indian real estate investment with a $4.5B market cap and 2) telecommunications in emerging markets

Built complete investment pitch books for 4 deals at advanced stages and liaised with 12 prospective international co-investors

Created Monte Carlo simulation using Pandas (Python) to generate 30,000 sample portfolios with 8+ constraints

Generated insights on customer churn and renewal rates from data tables with 100M rows in SQL

For more sample bullet points and details on how to write effective bullet points, see our articles on resume bullet points , how to quantify your resume and resume accomplishments .

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• Data Scientist CV Example
• Senior Data Scientist CV Example
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• Data Science Manager CV Example
• Data Science Vice President CV Example
• Junior Data Scientist CV Example
• Career Change into Data Science CV Example
• Step-by-Step Guide
• Skills and Keywords to Add
• Sample Bullet Points from Top CVs
• Related Data & Analytics Resumes
• All Resume Examples
• Explore Alternative and Similar Careers
• Data Scientist Cover Letter
• Data Scientist Interview Guide

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17 Data Scientist Resume Examples for 2024

• Data Scientist Resume
• Data Scientist Resumes by Experience
• Data Scientist Resumes by Role

Writing Your Data Scientist Resume

We’ve reviewed countless data scientist resumes and have made a concerted effort to distill what works and what doesn’t about each of them.

Our number one tip to create an effective data science resume is to quantify your impact on the business ! These 17 data scientist resume samples below and our  data scientist cover letter templates  can help you build a great job application in 2024, no matter your career stage.

Whether you’re looking for your first job as an entry-level data scientist or are a veteran with 10+ years of expertise, you’ll find plenty of tools to build your perfect resume, like our new  Word resume examples  or  free Google Docs resume templates .

Data Scientist Resume Example

or download as PDF

Why this resume works

• You need to  write your resume  in a way that  shows the employer that you’ve materially impacted the companies you’ve worked for.
• This means you should quantify your value in terms of business impact, not model performance. Model performance metrics without context really don’t convey much.
• They’re a way to quickly display your achievements and convince the employer that you’ll bring that same kind of energy to their team or company.

Entry-Level Data Scientist Resume

• Considering adding projects to your  entry-level data scientist resume  in lieu of enough work experience?
• You can demo the punch of a project by framing a question and then answering that question with data.
• Again, your results should be consistently expressed in numbers. Even if the result is as silly as saving 12 minutes per movie, it recognizes the importance of measuring impact.
• Customizing looks like: mentioning the target business by name and including relevant keywords from the  job description . 

Associate Data Scientist Resume

• When you have little to no professional background,  the skills you list on your resume  matter more than ever. And your abilities aren’t just selling points—they’re also a springboard for you to demonstrate your willingness to learn. 
• While writing your associate data scientist resume objective, immediately dive into any education or internship highlights with notable companies like Northrop Grumman. Then, sprinkle in some personality that shows your enthusiasm for new knowledge—drive and inquisitiveness are highly desirable traits in new professionals.

Senior Data Scientist Resume

• Your  senior data scientist resume  can really wow when you show a clear career progression from data analyst to data scientist to senior data scientist.
• That said, if you’ve got at least four years of experience under your belt, it’s fine for your work experience to account for about 70 percent of the page.
• A worthwhile summary should give a quick snapshot of your career highlights in two to three power-packed sentences and include the target company by name.

Data Scientist Intern Resume

• Call attention to your expertise in computer science by listing your proficiency in advanced programs like Keras on your data scientist intern resume.

Data Visualization Resume

• Whether it’s geospatial analysis, real-time data monitoring, or even creating standard visuals, make sure to quantify the impact of each and clearly state the benefit these tasks brought to the company to strengthen your data visualization resume.

Healthcare Data Scientist Resume

• Having two qualifications! Now’s the time to show all the degrees you’ve got! The best-case scenario is to have two degrees where one caters to the healthcare field while the other highlights your expertise in data science!

Amazon Data Science Resume

• Let that statement capture your aspirations and what you desire to bring to your new employer. Hiring managers are eager to see your passionate side and value to the team.

Python Data Scientist Resume

• Mentioning achievements such as improving project outcomes and reduction in process duration in your Python data scientist resume is a great way to leverage your experience honed over years of hard work.
• Then, by writing a great cover letter , you give yourself room to expound on exactly how you reduced process duration as a Python data scientist.

Data Scientist Machine Learning Resume

• Even if you already have ample experience in your field, you can give your data scientist machine learning resume a competitive edge by bringing your higher education to light. Create space to showcase your advanced degree in a relevant subject like statistics to further stand out.

Data Science Manager Resume

• Again, the results of your work should be stated clearly in terms of tangible impact (are you sensing a theme?). 
• Using a two-column layout for your  data science manager resume  allows more information to fit on a single page. Even with nine-plus years of experience, keeping your resume to one page is ideal.
• Fretting these details? Our  resume templates for 2024  may suit your specific needs; additionally, we’ve got 10 fresh and  free Google Docs resume templates  that can make your  resume-building  blues go away!.

NLP Data Scientist Resume

• When you’re trying to figure out  what to put on your resume  for a more specialized role like an NLP data scientist, it’s important you showcase your proficiency in operationalizing models to have a big impact on the business.
• Don’t focus on the technical aspects of the models you’ve built on your  NLP data scientist resume  (you’ll talk more about that in your interviews). Instead, take a step back and talk about the broad impact you’ve had in your previous roles.

Metadata Scientist Resume

• Prove your experience in programming, testing, modeling, and data visualization through well-designed projects that solve real problems through code.
• The key isn’t to reinvent the wheel but to create something dynamic and unique that isn’t easily replicated with a few Google searches and a video tutorial.
• Solve this problem with projects. If you’ve worked on excellent projects that used and showcased the necessary skills required for the job, list them and watch your resume bloom with confidence!

Educational Data Scientist Resume

• Think “well-rounded” as you write; you might include an exciting publication related to the job role, quickly outline your relevant experience or abilities, and conclude with how and why you’ll better the company through your new role. 
• Skills and certifications add credibility, but potential employers also want to know about your impact.
• If you performed evaluations, what improvements did you make afterward? If you integrated machine learning, what optimizations did you use it for?

Data Analytics Scientist Resume

• Your data scientist, analytics resume should target the list of requirements that companies in your state commonly request.
• For example, 18 out of 20  job descriptions  for data science, analytics in the state of California list Python, SQL, R, Tableau, and Hadoop (in that order) as required skills.
• After you add job-market-specific data, our  free resume checker  can assess your resume for other key elements like spelling, grammar, and active language. 

Data Science Consultant Resume

• To best represent your capabilities, use metrics to talk about your accomplishments.

Data Science Director Resume

• For an effective data science director resume, use a clean and simple resume template and format your work experience in reverse-chronological order. Doing so will put your most recent and relevant accomplishments at the top, making it the first thing a recruiter will look at.

Related resume guides

• Data Analyst
• Data Engineer
• Computer Science

Recruiters only spend an  average of seven-plus seconds reviewing your resume , so it’s vitally important that you catch their attention in that time. Our guide for 2024 takes you section by section through your resume to ensure you get that first interview.

You can successfully choose a winning  resume format in 2024  that will snag an employer’s attention.

Short on time? Here are the quick-hit summaries of each section you can apply to your resume:

• Whether for a company or yourself, what you’ve worked on should be the focus of your resume. Always try to include a measurable impact of your work.
• Make this the job title you’re looking for (e.g., “data scientist”), and don’t worry about a summary unless you’re making a career change.
• Only include technical skills that you’d be comfortable having to code with/in during an interview. Avoid a laundry list of different skills.
• Include relevant courses if you’re looking for an entry-level role. Otherwise, make your work the focus of your resume. If you went to a boot camp, list it here.
• Double-check everything. This is not the place you want to make a mistake. You don’t need to put your exact address. City, state, and zip are fine.
• Try to keep it to one page. Keep your bullets brief. Triple-check your grammar and spelling, and then have someone else read it.
• Read the  data scientist job description . See if any projects you’ve worked on come to mind while reading it. Incorporate those specific projects into your resume.

Your data science projects and work experience

Let’s jump right into the good stuff and talk about the most important part of your resume: your work experience and projects. This is it. This is the grand finale. This is where the person reviewing your resume decides whether or not you’ll get an interview.

When talking about your previous work (whether that’s for another employer or on a side project), your goal is to convince the person reviewing your resume that you’ll provide value to their company. This is not the place to be humble. We want to see that “I’m wearing my favorite outfit” level of confidence.

The template for successfully talking about your experience as a data scientist is:

• Clearly state the goal of the project
• You can mention the programming languages you used, the libraries, modeling techniques, data sources, etc.
• State the quantitative results of your project

You’re a data scientist, so highlight your value by demonstrating the quantitative impact of your work.  These can be estimates . For example, did you automate a report? Roughly how many hours of manual work did you save each month? Here are some ideas for how you can quantitatively talk about your projects:

Ways to define the impact of your data science work

• Example:  You developed a pricing algorithm that resulted in a $200k lift in annual revenue.
• Example:  You built a model to predict who would cancel their subscription and introduced an intervention to improve monthly retention from 90% to 93%.
• Example:  You built a marketing attribution model that helped the company focus on marketing channels that were working, resulting in 2,100 more users.
• Example:  You ran an experiment across different product features, which resulted in a 25% increase in engagement rate.
• Example:  As a side project, you built a movie recommendation engine that now saves you 26 minutes each time you need to decide which movie to watch.
• Example:  Since you built a customer segmentation model to determine how to communicate with different customer types, customer satisfaction is up 17%.

Numbers draw attention, are convincing, and make your resume more readable. Which of these two ways to describe reporting is more compelling?

• Used Python, SQL, and Tableau to conduct daily reporting for the business
• Using Python, SQL, and Tableau, combined 11 data sources into a comprehensive, real-time report that saved 10 hours of work weekly

If nothing else, please take this away from this guide:  state the results of your projects on your resume in numbers.

Trade-offs between projects and work experience

Simply put, the more work experience you have, the less space “projects” should take up as a section on your resume. In the sample resumes above, you’ll notice that only the more entry-level data scientist resumes have a section for projects.

The senior-level resumes focus on projects in the context of experience within companies. Real estate is precious on a one-page resume, so you’ll want to focus on the bullets that most clearly demonstrate how you’re a great fit for the job. Companies want to hire data scientists who have demonstrated success at other companies.

Entry-level data science projects for resume

Junior data scientists should include projects on their resumes. Try starting with a  resume outline , where you can brain dump anything and everything about your projects; then, you can distill the best of it into your final resume. Can you share the Github link? Do you have a link to a write-up you did about your project?

The more initiative you can show for entry-level data science projects, the better. Do you have any questions to which you’ve always wanted the answer? You can probably think of some clever ways to get data around that question and come up with a reasonable answer. For example, our co-founder wanted to know  which data science job boards were best , so he pulled together some data, laid out his assumptions and methodology, and made his conclusions.

No matter what projects you include on your resume, be sure to clearly state the question you were answering, the tools and technologies you used, the data you used to answer the question, and the quantitative outcome of the project. Succinctly stating conclusions and recommendations from your analysis is a highly sought-after skill by employers in data science.

The data scientist summary

Since you have limited space on your resume, you should only include a  resume objective  if you take the time to customize it for each role to which you apply.

You may want to include a  resume summary  or objective when you’re making a big career change. If you do include one, make sure to keep it specific about your goal and experience. This is valuable space you’re going to be using on this statement, so take the time to personalize it to each job.

Include the title of the job you’re looking for under your name. This should be aspirational. So if you’re a data analyst looking to apply for data scientist jobs, you would put “data scientist” under your name as the headline:

Skills that pay the bills

The most common mistake we see on data science resumes (that we used to make on our resumes) is what we call skill vomit. It’s a laundry list of skills in which no one person could have expertise. A quick rule of thumb:  if the skills section takes up a third of the page, it takes too much space. This is a big red flag for hiring managers.

The reason people make such an exhaustive skills section is to get through the mythical data science resume keyword filters. If you’re changing your resume in small ways for each job you apply to (for example, put Python for jobs that mention Python and R for jobs that list R if you know both), you’ll have no problem with those keyword filters.

The rule of thumb that we recommend you use in determining whether to include a skill on your resume is this:  i f it’s on your resume, you should be comfortable coding with/in it during an interview.

So that means if you’ve read a few articles on Spark or adversarial learning, but you can’t use them in code, they should not be on your resume. If you only have a handful of tools under your toolbelt, but you can use them effectively to answer questions with data, you’ll be able to find jobs looking for that skill set. 

We can assure you there are all kinds of data science jobs available. Our scraper that indexes jobs across thousands of company websites shows over 5,000+ full-time data science job openings in the US across all tenures and skill sets. And our scraper has a lot of room for improvement, so that’s significantly lower than the actual number. 

There are tons of fish in the job market sea; you just need a fishing rod.

Entry-level vs. senior skills sections

Generally, the more senior you are, the shorter your skills section needs to be. If you’re a senior data scientist, you should talk about the major tools and languages you use but save specific modeling techniques for the “Work Experience” section. Show how you used particular models in the context of your work.

When you’re more junior, you likely haven’t had the chance to use all of the techniques you’re comfortable with within work or a project. That’s okay! It’s expected. But you still want to make it clear to a potential employer that you can use those methods or libraries.

Education is a lot like skills in that the more senior you are as a data scientist, the less space the education section should take up on your resume. When you’re looking for one of your first data science jobs, you might want to include courses relative to data science to demonstrate you have a strong foundation.

Classes in subjects like linear algebra, calculus, probability, and statistics and any programming classes are directly relevant to being a data scientist. If you’re looking for your first job out of college, you should include your GPA on your resume. When you have a few years of work experience, it’s not necessary to include it.

If you just finished (or are finishing) a data science boot camp, this is the place to list where you went. You can include the relevant lessons or classes you took. Be sure to have a few projects from your boot camp (especially if it was an original project) in your resume’s “Projects” section.

Contact information

The takeaway from this section is simple:  this is not where you should make a mistake . Storytime! When our co-founder was first applying to jobs out of college, he realized about 20 applications in, he had spelled his name “Stepen” instead of “Stephen.” Don’t pull a Stepen.

Data suggests that when your email is wrong, your response rate from companies drops to zero percent. That’s just math. We’ve seen exactly four data science resumes where the email address on the resume was incorrect.

Make sure your email address is appropriate. While we don’t doubt the authenticity of your “ [email protected] ” email, maybe don’t use it when applying for jobs. To play it safe, stick to a combination of your name and numbers for your email.

This is the section you can include anything you want to show off for a data science role. Have a blog where you document the analysis you do for Dungeons & Dragons? Active on Github or an open-source project? Include a link to anything relevant to data that will help you stand out in your application.

General resume formatting tips

This section is just a list of one-off styling and formatting tips for your data science resume:

• Keep it brief. Bullets should be informative but should not drag on for paragraphs.
• Each bullet point in your resume should be a complete thought. You don’t have to have periods at the end of each bullet.
• Keep your tense consistent. If you’re referring to old projects in the past tense, do that for all old projects.
• Please, please don’t get your contact information wrong.
• Don’t give the person reviewing your resume a silly reason to put it in the “No” pile.  Check your resume  carefully.

Customization for each application

You don’t have to go overboard with your resume customization. Here are the steps we recommend to customize it for each job:

• So in this example, we’ll have one “Python” resume and one “R” resume depending on what the job is seeking.
• For example, if you have experience with attribution modeling and this is a marketing data science role, you should include that experience.
• Do you have experience with a certain library or modeling technique they mention? 
• Do you have experience in the domain of the specific job?
• Do you have any relevant industry experience with the company?

Let’s walk through a specific example to highlight what we mean by including particular projects for different jobs. Let’s say that a senior data scientist is applying for the position below.

In the “Ideally, you’d have” section, they mention they want someone who has “Experience with ETL tools.” Let’s say that in reality, the candidate had a large role in building out data pipelines in his fictional role as a senior data scientist at EdTech Company.

So all we’d do is change that section of his experience at EdTech Company to talk about that project, as you see below:

Data science resume customization example

Original bullet on the resume: Worked closely with the product team to build a production recommendation engine in Python that improved the average length on the page for users and resulted in $325k in incremental annual revenue

Customized for the role: Built out our company’s ETL pipeline with Airflow, which scaled to handle millions of concurrent users with robust alerting/ monitoring

Customization for startups

For early-stage startups (anything less than 50 employees), one of the most important qualities they’re looking for in a hire is ownership. That means they want someone who can ask a question and come up with an answer with minimal instruction. 

If you want to stand out to these companies, you should demonstrate ownership in the way you list projects on your resume. Include active words like “drove” or “built” instead of passive language like “worked on” or “collaborated on.” We know this seems nit-picky, but this matters to early-stage companies. Hiring managers at companies this size are strained for time and will use any signal to weed people out.

Concluding thoughts

There you have it—a compelling, easy-to-read data science resume built for 2024. Now you can celebrate by doing something as fun as  writing a resume . Maybe your taxes? Or go to the dentist?

By building or  updating your current resume , you took a huge step toward landing your next (or first) data science job. Now please, we beg you, check your grammar and spelling again and have someone else read your resume. Don’t let that be the reason you don’t get an interview.

Congrats! The first and hardest step is done. You have a data science resume! With great power comes great responsibility, so go and apply wisely.

Land your next job with our AI-powered, user-friendly tool.

Gut the guesswork in your job hunt. Upload your existing resume to check your score and make improvements. Build a resume with one of our eye-catching, recruiter-friendly templates.

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Resume Checker Resume Builder

Data Scientist Resume - Sample & Guide for 2024

You’re a data scientist. You solve complex problems.

Your newest problem: writing a resume for that elusive data scientist role.

Fortunately, you’ve arrived at the best place. This guide will take you through a range of steps, so you can create a data scientist resume that gets results. 

• An example of a finished data scientist resume that works
• How to write a data scientist resume that’ll fill up your interview diary
• How to make your data scientist resume stand out [with top tips & tricks]

Before we get stuck into the data, here’s a data scientist resume example, created with our very own online resume builder :

This resume performs as well as it looks. Just follow the steps in this guide to create a data scientist resume that gets great results, just like the above example.

Besides our data scientist resume example, we've got even more resume examples for professionals in the computer science field:

• Data Analyst Resume
• Data Entry Resume
• Computer Science Resume
• Artificial Intelligence Engineer Resume
• Engineering Resume
• Software Engineer Resume
• Web Developer Resume
• Java Developer Resume

How to Format a Data Scientist Resume

Before you can reveal why you’re the best person for the job, you need to pick the best format.

Now, this is more important than it sounds.

It will allow your best attributes to ‘jump off the page’ into the recruiters' vision. 

The most common resume format is “ reverse-chronological ”, and it’s for good reason. Essentially, it allows the recruiter to immediately see the value that you provide. We recommend the majority of individuals start with this format.

The following resume formats also get our approval:

• Functional Resume – If you have strong skills, but a weak work history, then this resume format is recommended. It’s ideal for skilled scientists that don’t have a lot of experience or have gaps in their employment history
• Combination Resume – Acting as a combination of both the “Functional” and “Reverse-Chronological” formats, you can use a combination resume if you have a wealth of work experience

Once you’ve chosen your format, you need to organize your resume layout .

Use a Data Scientist Resume Template

As a data scientist, you present data in a structured way.

The same needs to happen to your resume.

However, creating a structured file isn’t an easy task!

You could use Word, but then you will have to risk the layout falling apart with every small alternation. 

Want to skip formatting issues? Use a data scientist resume template .

What to Include in a Data Scientist Resume

The main sections in a data scientist resume are:

• Work Experience
• Contact Information

Want to go a step further? You can also add these optional sections:

• Awards & Certification

Interests & Hobbies

What should you write for each section? 

Read on to learn how.

Want to know more about resume sections? View our guide on What to Put on a Resume .

How to Correctly Display your Contact Information

Now, there is no need to get creative in this section. 

The only requirement is accuracy. 

An incorrect contact section may mean the recruiter can’t contact you – disaster! 

The contact information section on your resume must include:

• Title – In this case, “Data Scientist”
• Phone Number – Check this multiple times for errors
• Email Address – Use a professional email address ([email protected]), not your childhood email ([email protected]).
• (Optional) Location - Applying for a job abroad? Mention your location.
• Ellie Branning, Data Scientist. 101-358-6095. [email protected]
• Ellie Branning, Data Scientist Whizz. 101-358-6095. [email protected]

How to Write a Data Scientist Resume Summary or Objective

It’s safe to say that recruiter’s don’t have time to dig into the data of every resume.

Instead, they scan the resume for the main points.

In fact, studies have shown that recruiters spend just a few seconds on each resume! 

So, what can you do?

You need an introduction that makes your value ‘jump off the page’.

To do this, use a resume summary or objective .

These are snappy paragraphs that go on top of your resume, just under your contact information. 

Now, this section is extremely important. This small paragraph could be the deciding factor between scoring an interview and simply having your resume dismissed.

But what is the difference between the two sections?

A resume summary is a 2-4 sentence summary of your professional experiences and achievements.

Certified data scientist with 12 years of experience for a diverse clientele. Achievements include updating data streaming processes for an 18% reduction in redundancy, as well as improving the accuracy of predicted prices by 18%. Highly-skilled in data visualization, machine learning, leadership.

A resume objective is a 2-4 sentence snapshot of what you want to achieve professionally.

Motivated data scientist with 2+ years of experience as a freelance data scientist. Passionate about building models that fix problems. Relevant skills include machine learning, problem solving, programming, and creative thinking.

So, which one is best, summary or objective?

Generally, we recommend that experienced data scientists go with a resume summary. Those who are new to the field, like graduates and career changers, would be better suited to an objective. 

How to Make Your Data Scientist Work Experience Stand Out

Recruiters need to be confident that you will do a good job for the company.

Listing your work experience is the easiest and best way to do this.

Here’s the best way to structure your work experience section:

• Position name
• Company Name
• Responsibilities & Achievements

Data Scientist

03/2016 - 05/2019

• Improved the accuracy of predicted prices by 18%.
• Coordinated a team of 16 data scientists working on 4 different projects.
• Updated data streaming processes for a 18% reduction in redundancy.

To separate your resume from the other applicants, you should talk about your best achievements, not your daily tasks. Doing so will clearly show how you can benefit the company.

Instead of saying:

“Data streaming.”

“Updated data streaming processes for an 18% reduction in redundancy.”

As you can see, the first statement doesn’t effectively convey your achievements. It shows that you streamed data, but it doesn’t show the results of your work. 

The second statement shows that you managed to reduce the redundancy numbers. Hard numbers that prove your skills – can’t argue with that!

What if You Don’t Have Work Experience?

Maybe you’re trying to break into the data science field?

Or maybe, you have already worked in the industry, but never in this specific role?

Your experience is null .

A recruiter will want data scientists that they can rely on. Whether you have job experience or not, being able to show that you have the skills is the most important factor.

If you already have proof of your data science skills, feel free to link to them in your resume.

With that said, there is still time to create a portfolio.

Here are several ways you can show your talents (and even get paid for it):

• Start freelancing.
• Offer your skills to friends and family.
• Contribute to open source projects on GitHub.
• If the above doesn’t work, become your own client! Show your skills by creating mock projects.

Are you recent data scientist graduate? Make sure to check out our student resume guide !

Use Action Words to Make Your Data Scientist Resume POP!

…are all common words that the recruiter sees time and time again.

However, you want to separate your resume from the competition, which means using power words to make your achievements stand out:

• Conceptualized
• Spearheaded

How to Correctly List your Education

Every great resume needs an education section.

But don’t worry, there is nothing too complicated here.

Simply enter your education history in the follow format:

• Degree Type & Major
• University Name
• Years Studied
• GPA, Honours, Courses, and anything else you might want to add

BSc in Statistics

University of Bath

2012 - 2016

• Relevant Courses: Probability and Statistics, Generalised Linear Models, Applied Statistics

Now, you may have some questions on this section. If so, here are the answers to some of the most frequent questions that we get:

• What if I haven’t finished education yet?

Regardless of whether you’re a data science graduate or still studying, you should mention all years studied to date

• Should I include my high school education?

The general rule is to only include your highest form of education. So, include your high school education if you don’t have a relevant degree for data science

• What do I put first, my education or experience?

Experiences are the priority, so those go first. If you’re a recent graduate, you will likely need to start with education.

Need to know more? Check out our guide on how to list education on a resume .

Top 15 Skills for a Data Scientist Resume

When it comes to the skills section, the hiring manager has seen it all before.

In fact, they need a data scientist to help with the entire pile of data scientist resumes!

You see, everyone lists all of their skills, even those that related to the job.

Your skill section should highlight your top skills in a way that is specific to the role.

Here are some of the most common data scientist skills:

Hard Skills for a Data Scientist Resume:

• Data Analysis
• Data Visualization
• Quantitative Analysis
• Machine Learning
• Mathematics
• Probability
• Programming

Soft Skills for a Data Scientist Resume:

• Critical Thinking
• Communication
• Time-Management
• Collaboration
• Data scientists frequently use tools, such as Cloudera, PERL, and OpenRefine. If there are any tools or pieces of software that you’re an expert in, include them in your skills section.

Here’s a more comprehensive list of 101+ must-have skills this year .

What Else Can You Include in a Data Scientist Resume?

We’ve now covered every essential resume section .

Is it the absolute BEST it can be?

Doing a great job with the above sections should be enough to get you shortlisted, but adding a few of the following sections could be the major factor in whether you become their new data scientist or not.

Awards & Certifications

Have you won an award for your work in a field that relates to data science?

Have you completed any courses to improve your skills and knowledge?

If you said yes to any of the above, make sure to mention them in your resume!

Don’t worry if you don’t have any awards or certificates, there a few companies that allow users to do online certifications, like Google.

• “IBM Data Science” - Coursera Certificate
• Google Certified Professional Data Engineer – GCP
• Microsoft Professional Program Certificate in Data Science
• “Deep Learning” - Coursera Certificate
• “Critical Thinking Masterclass” - MadeUpUniversity

Even though it is very unlikely to need a second language, you may want to add a small languages section to your resume. 

You see, being able to speak a second language is always an impressive skill to a hiring manager. 

Rank the languages by proficiency:

• Intermediate

Now, you may be wondering, “why would a recruiter need to know about my love for kayaking?”

Well, your hobbies reveal more about who you are as a person.

A hobbies section is an easy way to add personality to your resume, so add one if you have the space.

Here’s which hobbies & interests you may want to mention.

Include a Cover Letter with Your Resume

Here the thing –

Cover letters still play an important role during the application process.

They provide a number of benefits, but the main reason for using a cover letter is to show the recruiter that you care about working for their company.

To create a winning cover letter, we must use the correct structure. 

Here’s what we recommend:

You should complete the following sections:

Personal Contact Information

Your full name, profession, email, phone number, location, and website (or Behance / Dribble).

Hiring Manager’s Contact Information

Full name, position, location, email.

Opening Paragraph

It’s no secret that hiring managers skim through resumes and cover letters. As such, you need to hook the reader within the first few sentences. Use concise language to mention:

• The position you’re applying for
• Your experience summary and best achievement to date

Once you’ve sparked the reader’s interest, you can get deeper into the following specifics:

• Why you chose this specific company
• What you already know about the company
• How your skills relevant for the role
• Which similar industries or positions have you worked in before

Closing Paragraph

Don’t just end the conversation abruptly, you should:

• Conclude the points made in the body paragraph
• Thank the hiring manager for the opportunity
• Finish with a call to action. This is a good way to start a conversation. A simple “At your earliest opportunity, I’d love to discuss more about how I can help company X” will work

Formal Salutations

End the cover letter in a professional manner. Something like “Kind regards” or “Sincerely” will be proficient.

For more inspiration, read our step-by-step guide on how to write a cover letter .

Key Takeaways

If you followed all of the above advice, you’ve given yourself the best possible chance of landing that data scientist role.

Let’s quickly summarize what we’ve learnt:

• Format your data scientist resume correctly by prioritizing the reverse-chronological format and then following the content layout guidelines
• Start your resume with a summary or objective to hook the recruiter
• In your work experience section, give attention to your best achievements, rather than your responsibilities
• Craft a convincing cover letter for an unbeatable application

Suggested Reading:

• How to Ace Interviews with the STAR Method [9+ Examples]
• 22+ Strengths and Weaknesses for Job Interviews
• What Is Your Greatest Accomplishment? [3 Proven Answers]

To provide a safer experience, the best content and great communication, we use cookies. Learn how we use them for non-authenticated users.

• • Turned data into actionable insights, providing C-suite stakeholders with insightful recommendations to streamline business operations and improve customer experience.
• • Generated statistical reports and visualizations, providing key insights for more than 20 marketing campaigns and initiatives, including A/B testing, customer retention, brand awareness, and global expansion.
• • Worked with senior leadership to develop and implement digital marketing strategy, identifying and implementing new tactics to improve campaign performance by 50%, resulting in 250% increase in revenue from search marketing campaigns.
• • Designed and implemented a machine learning system that predicts hardware malfunction with more than 80% accuracy.
• • Created global and personalized real time reports system for executives stakeholders and processes in SAS, Tableau, and proprietary systems.
• • Worked closely with a team of data engineers and BI analysts to improve the efficiency customer recommendation analytics engine by 33%.
• • Collected technical requirements for $500K+ customer accounts, defining data rules for and KPIs for performance metrics.
• • Perform HR data collection and a variety of statistical analyses using Microsoft Excel, SAS, Tableau and Python.
• • Assisted senior data science team in building innovative machine learning models and segmentations for personalization initiatives to drive margin, revenue, and conversion.

14 Data Scientist Resume Examples & Guide for 2024

Your data scientist resume needs to convey your expertise in data analysis and interpretation. Make sure to highlight your proficiency in programming languages such as Python or R. It's crucial that your experience with machine learning algorithms and data visualization tools like Tableau or PowerBI is evident. Your resume should reflect your ability to turn complex data into actionable insights.

All resume examples in this guide

Data Science Intern

Entry-Level Data Scientist

Senior Data Scientist

Machine Learning

Python Data Scientist

Associate Data Scientist

Data Science Manager

NLP Data Scientist

Metadata Scientist

Educational Data Scientist

Data Science Director

Data Science Consultant

Data Analytics Scientist

Senior Data Scientist | CAP | DASCA resume example

Resume Guide

Data Scientist Resume Example

Resume Format

Resume Experience Section

Hard & Soft Skills

Data Science Certifications

Resume Summary/Objective

Other Resume Sections

Key Takeaways

By Experience

Data science is a complex industry, and continues to evolve in today’s technological landscape.

Machine learning and ChatGPT may be booming right now, but it can be challenging to stay on top of these rapidly changing technologies.

Your data scientist resume needs to demonstrate your technical skills as well as your ability to communicate with others. Show the distinct value of each of your projects while avoiding redundancy.

Don’t worry, our guide will show you how to write an incredible data scientist resume that highlights your expertise in Python and SAS without overshadowing your interpersonal skills.

This guide will teach you:

• How to use our data scientist resume templates to make a good impression and attract recruiters’ attention.
• How to format your experience section so that hiring managers can see how you’ll impact success at their company
• How to showcase your skills in a way that shows you’re on top of industry trends and are the right candidate for your target job
• What recruiters look for and how to write a strong data scientist resume summary that gets callbacks

Looking for related resumes?

• Data Engineer Resumes ;
• Entry Level Data Analyst Resumes ;
• Tech Resumes ;
• SQL Developer Resumes ;
• Tableau Developer Resumes .

Data scientist resume example

How to format a data scientist resume

There are a few different resume formats to consider for your data scientist resume, but your best bet is to go with a reverse-chronological resume .

The focus of this format is on your work history listed in reverse-chronological order, just as the name suggests.

This is the best choice for senior data scientists who have been in the industry for 10 years or more. It brings attention to your career growth and shows your commitment to your work.

If you’re changing careers or just starting out as an entry-level candidate, a better choice is a functional resume format. This brings attention to your skills and away from your lack of experience.

When choosing a file format for your data scientist resume, always go with PDF. It provides extra security and ensures there won’t be any unwanted formatting changes.

Only use a DOC or another file format if the job application instructions explicitly say so.

As far as data scientist resume length, limit yours to one page. Recruiters don’t want to spend time reading through lengthy resumes, so stick to what’s relevant.

If hiring managers want to see more of your work, your GitHub link is there to guide them.

(CTA to ats checker - Enhancv to do)

The top sections on a data scientist resume:

What recruiters want to see on your data scientist resume:, how to create an impactful data scientist experience resume section.

The experience section is the core of your data scientist resume. It’s where you’ll let all your hard work shine.

To make the most impact possible, follow these key rules:

• Include only major and relevant positions - the 2-month stint behind the counter at your grandfather’s banana stand interests no one. But that job as a data engineer working on sales data for a national fruit reseller is something the recruiter needs to see!
• Make it reverse-chronological - it’s the resume standard, and it saves mental energy for the recruiter. List your most recent positions first.
• Focus on impact rather than responsibilities - data mining, statistical analysis, and data visualization will be on almost every data scientist’s resume. Instead, explain the impact you had rather than just listing job duties.

Let’s take a look at a data scientist resume experience section to see how to avoid a common mistake.

• • Created and presented models for loan success factors.
• • Did database manipulation of the Financial Aid Database.
• • Coordinated a team of data scientists.

What doesn’t work in this example:

• No quantitative metrics or measurable results
• Uses broad verbs like “did” and “coordinated” that don’t speak to success
• Leaves out industry-specific knowledge or skills

Let’s look at that example again with a few changes.

• • Designed and implemented models for loan success factors, achieving a 20% improvement of approval decision time.
• • Spearheaded complete database restructuring of the Financial Aid Database used across 16 different countries.
• • Coordinated a team of 20 data scientists working on 6 different projects for insurance, finance, marketing, and security departments.

What works in this example:

• Shows evidence of specific results by “achieving a 20% improvement of approval decision time”
• Shows project management skills by mention “team of 20 data scientists working on 6 different projects”
• Shows industry-specific “data restructuring” skills and reach of “16 different countries”

This version is a big improvement. It quantifies impact with measurable results and industry-specific skills.

Always focus on relevant achievements instead of general responsibilities and tailor every section of your resume to fit your target job.

How to quantify impact on your data scientist resume

Companies hire data scientists to provide solutions and maximize success. If you want hiring managers to give you a chance, you need to quantify impact on your resume.

Recruiters will be looking through a stack of resumes that all list “data visualization” and “algorithm development” as skills. It’s not enough just to list it. You need to prove it.

Provide evidence to support your claims by sharing specific achievements with measurable success. Use real data and numbers to quantify impact in every section of your resume.

Quantitative data that can strengthen your data scientist resume include:

• Increased sales revenue
• Reduced redundancy or errors
• Rate of engagement or number of users
• Improved algorithm accuracy
• Profit margin
• Time saved for the company
• ROI for projects

Use these metrics throughout your resume to show potential employers exactly how you’ve achieved succes in previous roles.

Writing an entry-level data science resume

Just because you’re a recent grad looking for your first job in data science, don’t start thinking “I’m done, I don’t have any experience yet!”.

You’re mistaken if you think you don’t have any experience. Consider including

• Course projects that involved data science work - surely you’ve practiced your skills on a few practical exercises you can list here. Just make sure you feature the new and exciting projects - no one wants to see the same tired Titanic Survivor project!
• Internships - no matter if it’s your uncle’s company or a university help gig, you probably learned a lot, including keeping up with deadlines, working well with others, and communicating data results to different audiences. Practical skills matter, even if they’re soft skills.
• Volunteer work or side projects - if you don’t have practical experience, create some. There are tons of local SaaS startups that would benefit from logistic regression analysis to uncover their user activation points - help them out and use that as a practical example in your resume.

As you can see, there is a lot going on beyond traditional 9-to-5 steady job experience. And all of these will look great on your data scientist resume!

Looking to build your own entry-level job resume? Follow the steps in our guide on How To Write Your First Job Resume .

How to list your hard skills and soft skills on your resume

A data scientist needs a unique set of skills that lets you explore, transform, visualize and model datasets, and also communicate constantly with diverse stakeholder groups.

Make a good impression by showing that you have the right combination of hard skills and soft skills to accomplish this.

In “ Top 10 Big Data Skills to Get Big Data Jobs ” Amit Verma presents a comprehensive list of languages and systems data scientists should be able to work with, including

Top data scientist technical skills

Make sure you include only things that you know well enough to start working with tomorrow. There’s no point in inflating expectations and then missing the mark.

What about soft skills?

Just knowing the technology won’t cut it, you need soft skills too. We list some great ones below, and you can check out KDnuggets list of important soft skills .

Data scientist soft skill examples

The world of data is complex. Demonstrate that you can navigate through it, but also help others orient themselves in it. Make sure you cover this, especially for more senior positions where presenting to managers is everyday work.

How to list your certifications and education on your resume

You’ve come a long way to becoming a data scientist. You’ve put in a ton of hours reading O’Reilly textbooks, debugging Python scripts, and creating visualizations in Tableau.

Make all your hard work show on your resume. For a stellar education section, add info on

• Your university and major
• Your GPA and final marks
• Key courses relevant to the position you’re applying for
• Any awards you received or societies you were part of

Since data science is a relatively new field, it’s common for professionals to come into it from different fields. If this is the case for you, you can shorten your education section and include additional courses and certifications you’ve earned.

Top 20 data scientist certifications you can take:

Make sure you follow a few rules when presenting certifications on your resume:

• Make them stand out - don’t bury your certifications in another resume section, give them their own
• Add any capstone projects you worked on - certifications usually make you show what you learned in practice, prove that you can do what you say
• Show them your drive - if you completed the certification course quickly, mention it on your resume. It shows dedication and motivation to learn.

How to write your data scientist resume summary or objective

You may have heard the terms summary and objective used interchangeably when talking about resumes.

To get specific, a summary typically captures your industry experience and a few career highlights in 2-3 sentences. An objective talks about what you want to achieve in the future.

These days they are usually combined into one statement and referred to by either name.

A good formula for your data scientists resume summary is to write 2-3 sentences that cover the following points:

• Your title and role in the industry
• A top career highlight
• A shared goal of you and your potential employer

Let’s look at an example that uses this template.

• Specific number of years of experience and industry focus
• Shares a measurable result that achieved “94% accuracy”
• States shared goal to “increase engagement with Python modules” with target employer

Additional sections for a data scientist resume

Depending on your experience and career path, there may be additional sections you want to include on your resume.

• Projects - including a section for projects can be key in increasing the value of a data scientist resume. Potential employers want to know how you’ve used your practical skills, and a successful project is a great way to show that.
• Awards - important industry achievements or competitive awards can be a great way to show your value. Include any relevant awards you’ve earned in the field.
• Volunteer work or hobbies - not all practical experience has to come from a job. There are plenty of ways you can develop relevant skills through volunteering or hobbies. Include any experiences that speak to your industry knowledge.
• Publications - a good data scientist is a clear communicator as well as a numbers person. Publications will highlight your ability to clearly communicate complex ideas.

Remember that publications aren’t just research papers published in peer-reviewed journals. This section can also include links to blog posts you’ve written to show that you can speak in more than just an academic tone.

Key takeaways for writing a competitive data scientist resume

To sum it all up, a great data scientist resume should tick these boxes:

• Make a good impression and show your body of work with links to your GitHub portfolio and LinkedIn profile
• Demonstrate practical knowledge and quantify impact with measurable results so hiring managers will know that you can achieve success
• Show how your skills align with the requirements in the job description by tailoring every section of your data scientist resume to your target job
• Include additional sections that show that you stay on top of industry trends and are the right candidate for the job

Now you’re ready to create your amazing data scientist resume and land an interview for your next job!

Data Scientist resume examples

Explore additional data scientist resume samples and guides and see what works for your level of experience or role.

Looking to build your own Data Scientist resume?

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Entry Level Data Scientist CV Example

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CV Tips for Entry Level Data Scientists

• Highlight Your Education and Certifications : Detail your degree, major, and any relevant coursework in data science. Include certifications like Certified Data Scientist or Certified Analytics Professional to show your commitment to the field.
• Showcase Your Technical Skills : List proficiency in programming languages like Python, R, or SQL, and tools like TensorFlow, Tableau, or Hadoop. Mention your knowledge of machine learning algorithms, data visualization, and statistical analysis.
• Quantify Your Achievements : Use numbers to demonstrate the impact of your projects or internships, such as "Developed a predictive model that improved sales forecast accuracy by 20%" or "Implemented a data cleaning process that reduced errors by 15%".
• Customize Your CV to the Role : Tailor your CV to match the job description, emphasizing relevant skills and experiences. If the job requires experience with big data, highlight any projects or coursework that involved handling large datasets.
• Demonstrate Soft Skills : Mention teamwork, problem-solving, or your ability to communicate complex data insights in simple terms. These skills are crucial for data scientists to work effectively within a team and present findings to non-technical stakeholders.

The Smarter, Faster Way to Write Your CV

• Developed and implemented machine learning models for predictive analysis, resulting in a 30% increase in marketing campaign effectiveness.
• Collaborated with cross-functional teams to translate complex data into actionable insights, leading to a 20% improvement in operational efficiency.
• Managed a large dataset of over 1 million records, improving data quality and accuracy by 15% through rigorous data cleaning and preprocessing techniques.
• Conducted comprehensive data analysis using Python and R, leading to the discovery of key business insights that increased sales by 10%.
• Designed and developed a custom data visualization dashboard using Tableau, enhancing the company's data-driven decision-making process.
• Automated data extraction and transformation processes, reducing the time spent on data preparation by 40%.
• Assisted in the development of a predictive analytics model that increased the accuracy of sales forecasts by 25%.
• Performed exploratory data analysis on large datasets, identifying key trends and patterns that informed strategic business decisions.
• Participated in the creation of a data warehouse, improving data accessibility and usability across the organization.
• Machine Learning
• Data Analysis
• Data Visualization
• Python and R Programming
• Data Cleaning and Preprocessing
• Predictive Analytics
• Data Warehouse Development
• Automated Data Extraction and Transformation
• Cross-Functional Collaboration
• Tableau Proficiency

Entry Level Data Scientist CV Template

• Collaborated with [teams/departments] to [achieve a specific goal, e.g., improve data quality, streamline data processing], demonstrating strong [soft skill, e.g., teamwork, problem-solving].
• Conducted [type of data analysis, e.g., predictive modeling, machine learning algorithms] on [specific data set or project, e.g., customer behavior data, sales forecasts], leading to [measurable outcome, e.g., improved business insights, increased efficiency].
• Implemented [data management or improvement initiative, e.g., data cleaning protocols, new data visualization techniques], resulting in [quantifiable benefit, e.g., 20% increase in data accuracy, enhanced report detail].
• Contributed to [project or initiative, e.g., data-driven decision-making, AI model development], which led to [measurable impact, e.g., improved business strategies, increased revenue].
• Performed [type of data analysis, e.g., statistical analysis, data mining], using [analytical tools/methods, e.g., Python, R] to inform [decision-making/action, e.g., product development, marketing strategies].
• Played a key role in [task or responsibility, e.g., data collection, data preprocessing], ensuring [quality or standard, e.g., data integrity, data security] across all datasets.
• Major: Name of Major
• Minor: Name of Minor

100+ Free Resume Templates

How to format a entry level data scientist cv, start with a clear objective, highlight education and certifications, detail relevant experience and internships, emphasize technical skills and soft skills, include a portfolio link, personal statements for entry level data scientists, entry level data scientist personal statement examples, what makes a strong personal statement.

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CV FAQs for Entry Level Data Scientists

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Data Scientist Resume Examples For 2024 (20+ Skills & Templates)

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Looking to score a job as a Data Scientist?

You're going to need an awesome resume. This guide is your one-stop-shop for writing a job-winning Data Scientist resume using our proven strategies, skills, templates, and examples.

All of the content in this guide is based on data from coaching thousands of job seekers (just like you!) who went on to land offers at the world's best companies.

If you want to maximize your chances of landing that Data Scientist role, I recommend reading this piece from top to bottom. But if you're just looking for something specific, here's what's included in this guide:

• What To Know About Writing A Job-Winning Data Scientist Resume
• The Best Skills To Include On A Data Scientist Resume

How To Write A Job-Winning Data Scientist Resume Summary

How to write offer-winning data scientist resume bullets.

• 3 Data Scientist Resume Examples

The 8 Best Data Scientist Resume Templates

Here's the step-by-step breakdown:

Data Scientist Resume Overview: What To Know To Write A Resume That Wins More Job Offers

What do companies look for when they're hiring a Data Scientist?

Companies look for candidates with strong technical skills in programming languages like Python or R and experience with data manipulation, statistical analysis, and machine learning models. Companies are also looking for data scientists with problem-solving skills who can obtain actionable insights from complete datasets.

Your resume should show the company that your personality and your experience encompass all these things.

Additionally, there are a few best practices you want to follow to write a job-winning Data Scientist resume:

• Tailor your resume to the job description you are applying for: Tailor your resume for each application, aligning your skills with the specific requirements of each job description.
• Detail previous experiences: Provide detailed descriptions of your roles, emphasizing hard and soft skills related to the job description.
• Bring in your key achievements: Showcase measurable achievements in previous roles and share your best work.
• Highlight your skills:   Highlight your skills in Sales, Marketing, Communication, Customer Experience, and Management.
• Make it visually appealing: Use a professional and clean layout with bullet points for easy readability. Also, ensure formatting and font consistency throughout the resume and limit it to one or two pages.
• Use keywords: Incorporate industry-specific keywords from the job description to pass through applicant tracking systems (ATS) and increase your chances of being noticed by hiring managers.
• Proofread your resume: Thoroughly proofread your resume to eliminate errors (I recommend Hemingway App and Grammarly ). Consider seeking feedback from peers or mentors to ensure clarity and effectiveness!

Let's dive deeper into each of these so you have the exact blueprint you need to see success.

The Best Data Scientist Skills To Include On Your Resume

Keywords are one of the most important factors in your resume. They show employers that your skills align with the role and they also help format your resume for Applicant Tracking Systems (ATS).

If you're not familiar with ATS systems, they are pieces of software used by employers to manage job applications. They scan resumes for keywords and qualifications and make it easier for employers to filter and search for candidates whose qualifications match the role.

If you want to win more interviews and job offers, you need to have a keyword-optimized resume. There are two ways to find the right keywords:

1. Leverage The 20 Best Data Scientist Keywords

The first is to leverage our list of the best keywords and skills for a Data Scientist resume.

These keywords were selected from an analysis of real Data Scientist job descriptions sourced from actual job boards. Here they are:

• Data Science
• Communication
• Machine Learning
• Engineering
• Cross-Functional
• Organization
• Collaboration
• Descision Making

2. Use ResyMatch.io To Find The Best Keywords That Are Specific To Your Resume And Target Role

The second method is the one I recommend because it's personalized to your specific resume and target job.

This process lets you find the exact keywords that your resume is missing when compared to the individual role you're applying for.

Here's how it works:

• Open a copy of your updated Data Scientist resume
• Open a copy of your target Data Scientist job description
• In the widget below, paste your resume on the left, paste the job description on the right, and hit scan!

ResyMatch is going to scan your resume and compare it to the target job description. It's going to show you the exact keywords and skills you're missing as well as share other feedback you can use to improve your resume.

If you're ready to get started, use the widget below to run your first scan and get your free resume score:

Copy/paste or upload your resume here:

Click here to paste text

Upload a PDF, Word Doc, or TXT File

Paste the job post's details here:

Scan to compare and score your resume vs the job's description.

Scanning...

And if you're a visual learner, here's a video walking through the entire process so you can follow along:

Employers spend an average of six seconds reading your resume.

If you want to win more interviews and offers, you need to make that time count. That starts with hitting the reader with the exact information they're looking for right at the top of your resume.

Unfortunately, traditional resume advice like Summaries and Objectives don't accomplish that goal. If you want to win in today's market, you need a modern approach. I like to use something I can a “Highlight Reel,” here's how it works.

Highlight Reels: A Proven Way To Start Your Resume And Win More Jobs

The Highlight Reel is exactly what it sounds like.

It's a section at the top of your resume that allows you to pick and choose the best and most relevant experience to feature right at the top of your resume.

It's essentially a highlight reel of your career as it relates to this specific role! I like to think about it as the SportsCenter Top 10 of your resume.

The Highlight Reel resume summary consists of 4 parts:

• A relevant section title that ties your experience to the role
• An introductory bullet that summarizes your experience and high-level value
• A few supporting “Case Study” bullets that illustrate specific results, projects, and relevant experience
• A closing “Extracurricular” bullet to round out your candidacy

For example, if we were writing a Highlight Reel for a Data Scientist role, it might look like this:

The first bullet includes the candidate's years of experience in the role and wraps up with a value-driven pitch about how they've helped companies in the past.

The next two bullets are “Case Studies” of specific results they drove at their company. The last bullet wraps up with extracurricular information.

This candidate has provided all of the info any employer would want to see right at the very top of their resume! The best part is that they can customize this section for each and every role they apply for to maximize the relevance of their experience.

Here's one more example of a Data Scientist Highlight Reel:

The content of this example showcases a candidate transitioning from sales to data science, leveraging their experience with sales and bringing in measurable results in each bullet point. Then, they wrap up with a high-value extracurricular activity that's related to their target position.

If you want more details on writing a killer Highlight Reel, check out my full guide on Highlight Reels here.

Bullets make up the majority of the content in your resume. If you want to win, you need to know how to write bullets that are compelling and value-driven.

Unfortunately, way too many job seekers aren't good at this. They use fluffy, buzzword-fill language and they only talk about the actions that they took rather than the results and outcomes those actions created.

If you apply this framework to each of the bullets on your resume, you're going to make them more compelling and your value is going to be crystal clear to the reader. For example, take a look at these resume bullets:

❌ Data Scientist with 5+ years of experience.

✅ Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%.

The second bullet makes the candidate's value  so much more clear, and it's a lot more fun to read! That's what we're going for here.

That said, it's one thing to look at the graphic above and try to apply the abstract concept of “35% hard skills” to your bullet. We wanted to make things easy, so we created a tool called ResyBullet.io that will actually give your resume bullet a score and show you how to improve it.

Using ResyBullet To Write Crazy Effective, Job-Winning Resume Bullets

ResyBullet takes our proprietary “resume bullet formula” and layers it into a tool that's super simple to use. Here's how it works:

• Head over to ResyBullet.io
• Copy a bullet from your resume and paste it into the tool, then hit “Analyze”
• ResyBullet will score your resume bullet and show you exactly what you need to improve
• You edit your bullet with the recommended changes and scan it again
• Rinse and repeat until you get a score of 60+
• Move on to the next bullet in your resume

Let's take a look at how this works for the two resume bullet examples I shared above:

First, we had, “Data Scientist with 5+ years of experience.” 

ResyBullet gave that a score of 35/100.  Not only is it too short, but it's missing relevant skills, compelling language, and measurable outcomes:

Now, let's take a look at our second bullet,  “Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%”.

ResyBullet gave that a 61 / 100. Much better! This bullet had more content focused on the experience in the Data Scientist role, while also highlighting measurable results:

Now all you have to do is run each of your bullets through ResyBullet, make the suggested updates, and your resume is going to be jam-packed with eye-popping, value-driven content!

If you're ready, grab a bullet from your resume, paste it into the widget below, and hit scan to get your first resume bullet score and analysis:

Free Resume Bullet Analyzer

Learn to write crazy effective resume bullets that grab attention, illustrate value, and actually get results., copy and paste your resume bullet to begin analysis:, 3 data scientist resume examples for 2024.

Now let's take a look at all of these best practices in action. Here are three resume examples for different situations from people with different backgrounds:

Data Scientist Resume Example #1: A Traditional Background

Data Scientist Resume Example #2: A Non-Traditional Background

For our second Data Scientist Resume Example, we have a candidate who has a non-traditional background. In this case, they come from a background in sales but leverage experiences that have helped them transition to a Data Scientist role. Here's an example of what their resume might look like:

Data Scientist Resume Example #3: Data Scientist New Grad

For our third Data Scientist Resume Example, we have a new graduate who's never worked for a company before but has worked on several self-initiated projects. Here's an example of what their resume might look like when applying for Data Scientist roles:

At this point, you know all of the basics you'll need to write a Data Scientist resume that wins you more interviews and offers. The only thing left is to take all of that information and apply it to a template that's going to help you get results.

We made that easy with our ResyBuild tool . It has 8 proven templates that were created with the help of recruiters and hiring managers at the world's best companies. These templates also bake in thousands of data points we have from the job seekers in our audience who have used them to land job offers.

Just click any of the templates below to start building your resume using proven, recruiter-approved templates:

Free Job-Winning Resume Templates, Build Yours In No Time .

Choose a resume template below to get started:.

Key Takeaways To Wrap Up Your Job-Winning Data Scientist Resume

You made it! We packed a lot of information into this post so I wanted to distill the key points for you and lay out next steps so you know exactly where to from here.

Here are the 5 steps for writing a job-winning Data Scientist resume:

• Start with a proven resume template from ResyBuild.io
• Use ResyMatch.io to find the right keywords and optimize your resume for each role you apply to
• Open your resume with a Highlight Reel to immediately grab your target employer's attention
• Use ResyBullet.io to craft compelling, value-driven bullets that pop off the page
• Compare the draft of your resume to the examples on this page to make sure you're on the right path
• Use a tool like HemingwayApp or Grammarly to proofread your resume before you submit it

If you follow those steps, you're going to be well on your way to landing more Data Scientist interviews and job offers.

Now that your resume is taken care of, check out my guide on how to get a job anywhere without applying online!

Paula Martins

Paula is Cultivated Culture's amazing Editor and Content Manager. Her background is in journalism and she's transitioned from roles in education, to tech, to finance, and more. She blends her journalism background with her job search experience to share advice aimed at helping people like you land jobs they love without applying online.

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Entry Level Data Scientist CV example

As a budding Data Scientist, your CV should quantify your academic achievements and any practical data projects.

Our guide, complemented by an Entry-level Data Scientist CV example, will help you model a profile that predicts success in your job search.

CV templates 

Entry Level Data Scientist CV example

This is a good example of a Entry Level Data Scientist CV which is professionally formatted, and structured in a way that allows recruiters to easily find and understand the candidate’s key selling points.

Take some time to look at this CV and refer to it throughout the writing of your own CV for best results.

Entry Level Data Scientist CV format and structure

Your CV is the first impression you’ll make on anybody who reads it.

A disorganised, cluttered and barely-readable CV could seriously decrease your chances of landing interviews, so it’s essential to make sure yours is slick, professional and easy to navigate.

You can do this by using a clear structure and formatting your content with some savvy formatting techniques – check them out below:

Tips for formatting your Entry Level Data Scientist CV

• Length: Your CV should always be limited to two sides of A4, regardless of whether you have a year or three decades of experience. With recruiters juggling multiple responsibilities, they don’t have time to sift through lengthy applications.
• Readability : Make sure your CV is easy to read and looks professional by applying some simple formatting tricks. Bullet points are great for making large paragraphs more digestible, while formatting your headings with bold or coloured text will help the reader to find the information they need, with speed.
• Design & format: Your CV needs to look professional, sleek and easy to read. A subtle colour palette, clear font and simple design are generally best for this, as fancy designs are often harder to navigate.
• Photos: You can add a profile photo to your CV, if you want to add some personality to it, but they are not a requirement the UK, so you don’t have to.

Quick tip: Creating a professional CV style can be difficult and time-consuming when using Microsoft Word or Google Docs. To create a winning CV quickly, try our quick-and-easy CV Builder and use one of their eye-catching professional CV templates.

CV structure

Divide your CV into the following major sections when writing it:

• Name and contact details  – Head your CV with your name and contact details, to let the reader know who you are and how to contact you.
• CV profile – A brief paragraph which summarises your skills and experience and highlights why you’re a good match for the role.
• Core skills list – A snappy, bullet-pointed list of your most relevant skills.
• Work experience – A structured list of your work experience in reverse chronological order.
• Education – A summary of any relevant qualifications or professional training you’ve completed.
• Hobbies and interests – An optional section, which should only be used if your hobbies are relevant to the jobs you’re applying to.

Now you understand the basic layout of a CV, here’s what you should include in each section of yours.

Contact Details

Kick-start your CV with your contact details, so recruiters can get in touch easily. Here’s what you should include:

• Mobile number
• Email address – Make sure it’s professional, with no silly nicknames.
• Location – Your town or city is sufficient, rather than a full address.
• LinkedIn profile or portfolio URL – Ensure they’ve been updated and are looking slick and professional.

Quick tip: Avoid listing your date of birth, marital status or other irrelevant details – they’re unnecessary at this stage.

Entry Level Data Scientist CV Profile

Make a strong first impression with recruiters by starting your CV with an impactful profile (or personal statement for junior applicants).

This short introduction paragraph should summarise your skills, experience, and knowledge, highlighting your suitability for the job.

It should be compelling enough to encourage recruiters to read through the rest of your CV.

How to write a good CV profile:

• Make it short and sharp: Aim for a short, snappy paragraph of 3-5 lines. This is just enough room to showcase why you’d make the perfect hire, without going into excessive detail and overwhelming busy recruiters.
• Tailor it: Before writing your CV, make sure to do some research. Figure out exactly what your desired employers are looking for and make sure that you are making those requirements prominent in your CV profile, and throughout.
• Don’t add an objective: You only have a small space for your CV profile, so avoid writing down your career goals or objectives. If you think these will help your application, incorporate them into your cover letter instead.
• Avoid generic phrases: Focus on fact, not fluff. Phrases like “Committed and enthusiastic thought-leader” and “Dynamic problem solver” might sound fancy, but they’ll do nothing for your application. Not only do they sound cheesy, but they have no substance – stick to real skills and facts

Example CV profile for Entry Level Data Scientist

What to include in your entry level data scientist cv profile.

• Experience overview: Start with a brief summary of your relevant experience so far. How many years experience do you have? What type of companies have you worked for? What industries/sectors have you worked in? What are your specialisms?
• Targeted skills: Highlight your skills which are most relevant to Entry Level Data Scientist jobs, to ensure that recruiters see your most in-demand skills as soon as they open your CV.
• Important qualifications: If you have any qualifications which are highly relevant to Entry Level Data Scientist jobs, then highlight them in your profile so that employers do not miss them.

Quick tip: If you are finding it difficult to write an attention-grabbing CV profile, choose from hundreds of pre-written profiles across all industries, and add one to your CV with one click in our quick-and-easy CV Builder . All profiles are written by recruitment experts and easily tailored to suit your unique skillset.

Core skills section

To ensure that your most relevant skills catch the eye of readers, create a core skills section below your profile.

This section should be presented in 2-3 columns of bullet points highlighting your applicable skills. Before crafting this section, carefully examine the job description and create a list of any required skills, specialisms, or knowledge.

Use this list to include the necessary information in your section and present yourself as the ideal match for the position.

Important skills for your Entry Level Data Scientist CV

Data Analysis Proficiency – Practiced in analysing large datasets, extracting valuable insights, and identifying trends to inform data-driven decision-making.

Machine Learning Application – Capable of applying machine learning algorithms to solve complex problems, optimise processes, and enhance predictive modelling.

Statistical Modelling – Experienced in statistical modelling techniques, employing methods such as regression analysis and hypothesis testing for robust data interpretation.

Data Visualisation Mastery – Accomplished in creating visually compelling data visualisations, utilising tools like Tableau or Matplotlib to communicate findings effectively.

Python Programming Competence – Competent in utilising Python programming for data manipulation, analysis, and implementation of machine learning algorithms.

SQL Database Querying – Fluency in querying relational databases using SQL, ensuring efficient extraction and manipulation of data for analytical purposes.

Natural Language Processing (NLP) Understanding – Familiar with natural language processing techniques, applying them to extract meaningful information from unstructured text data.

Big Data Technologies – Knowledgeable about big data technologies such as Apache Spark or Hadoop, facilitating the processing of large-scale datasets.

Cloud Computing Integration – Proficient in integrating data science solutions with cloud computing platforms like AWS or Azure for scalable and efficient operations.

Experimental Design Capability – Well-versed in designing and conducting experiments, ensuring controlled testing conditions and valid results in data science projects.

Quick tip: Our quick-and-easy CV Builder has thousands of in-demand skills for all industries and professions, that can be added to your CV in seconds – This will save you time and ensure you get noticed by recruiters.

Work experience

By now, you’ll have hooked the reader’s attention and need to show them how you apply your skills and knowledge in the workplace, to benefit your employers.

So, starting with your most recent role and working backwards to your older roles, create a thorough summary of your career history to date.

If you’ve held several roles and are struggling for space, cut down the descriptions for your oldest jobs.

Structuring each job

Lengthy, unbroken chunks of text is a recruiters worst nightmare, but your work experience section can easily end up looking like that if you are not careful.

To avoid this, use my tried-and-tested 3-step structure, as illustrated below:

Start with a brief summary of your role as a whole, as well as the type of company you worked for.

Key responsibilities

Use bullet points to detail the key responsibilities of your role, highlighting hard skills, software and knowledge wherever you can.

Keep them short and sharp to make them easily digestible by readers.

Key achievements

Round up each role by listing 1-3 key achievements , accomplishments or results.

Wherever possible, quantify them using hard facts and figures, as this really helps to prove your value.

Sample job description for an Entry Level Data Scientist CV

Completed a 3-month summer internship working at a leading research centre specialising in the development and implementation of data capture systems.

Key Responsibilities

• Developed and maintained SQL databases for data storage and retrieval
• Worked as part of a team to develop predictive models using Python
• Shadowed senior data scientists and contributed to research projects
• Conducted exploratory data analysis to identify trends and insights

Quick tip: Create impressive job descriptions easily in our quick-and-easy CV Builder by adding pre-written job phrases for every industry and career stage.

Education section

In your education section, make any degrees, qualifications or training which are relevant to Entry Level Data Scientist roles a focal point.

As well as mentioning the name of the organisation, qualification titles and dates of study, you should showcase any particularly relevant modules, assignments or projects.

Hobbies and interests

Although this is an optional section, it can be useful if your hobbies and interests will add further depth to your CV.

Interests which are related to the sector you are applying to, or which show transferable skills like leadership or teamwork, can worth listing.

On the other hand, generic hobbies like “going out with friends” won’t add any value to your application, so are best left off your CV.

Creating a strong Entry Level Data Scientist CV requires a blend of punchy content, considered structure and format, and heavy tailoring.

By creating a punchy profile and core skills list, you’ll be able to hook recruiter’s attention and ensure your CV gets read.

Remember that research and relevance is the key to a good CV, so research your target roles before you start writing and pack your CV with relevant skills.

Best of luck with your next application!

For enquiries call:

+1-469-442-0620

• Data Science

Data Science Personal Statement Sample and Examples

Home Blog Data Science Data Science Personal Statement Sample and Examples

Data science is one of the most popular career options for students, especially those pursuing a Bachelor's degree. It is also one of the most sought-after courses in universities today. If you just want some ideas on how to write a personal statement for data science, then this article is for you. Also, if you’re someone who is willing to secure a career in the field of data science, then it is recommended that you pursue Data Science Courses that will enable you to learn all aspe that will enable you to learn all aspects and principles of data science. 

What is a Data Science Personal Statement?

In a nutshell, the personal statement for data science is a document that you write to explain why you are interested in pursuing the subject and what you can bring to the table. It should be written in a way that shows your interest in the subject and why you want to study it. You may want to include information about the following in your data science personal statement.  

• What led up to your decision to pursue this field? 
• Why do data scientists matter? What problems need solving by them? What value do they provide society as well as individuals? 
• How will studying this specific field help prepare you for future careers or additional educational opportunities (e-learning programs, etc.)?

You can also include the following:  

• What are your goals for this degree?  
• How will it benefit you?  
• What do you hope to achieve from studying data science?  
• Why is this field important in today’s society?  
• What are the challenges that you see in this field?  
• How will you address those challenges?  
• What do you think the future of data science is?  
• How do you plan on staying relevant as technologies and trends change?

The Importance of Creating a Data Science Personal Statement

Data science personal statement is a formal document that will be used by the company to evaluate your skills. If you are applying for a Data Science job and want to impress the hiring manager, then you must write a strong data science personal statement.

A good personal statement for a master's in data science must be unique, creative, informative and interesting to read. It should describe not only your skills and experience but also showcase your ability to think critically and creatively.

A well-written data science personal statement will help you stand out from other applicants and make yourself an ideal candidate for the job that you want. Here are some useful tips for writing a strong data science personal statement: 

• Be honest and straightforward in your personal statement. 
• Don’t exaggerate or lie about your skills, experience and achievements. If you don’t have any relevant work experience, then focus on other areas where you can showcase your skills, such as volunteering or community projects. 
• Know the company that you are applying to and tailor your personal statement accordingly. A generic resume won’t help if you are applying for a specific job position. Instead, write a customized letter that shows how well-suited you are for this role. 
• Keep it short and sweet. The best personal statements are between a few hundred to a few thousand words long. Don’t try to cram everything in one big paragraph; break it up into smaller sections that will make it easier for readers to digest. 

So now you might have understood how important data analytics personal statements are. To learn how to create a personal statement, it is recommended that you enroll in the Best Data Science Bootcamps . 

Data Science Personal Statement Sample

I am writing this Data Science Personal Statement for the MS in Data Science program at UC Berkeley. My goal is to explain why I want to pursue a career in data science and how my experience as an undergraduate student has prepared me for graduate school. As you can see from my resume, I have had many opportunities to work with large amounts of data through internships and research projects over the course of my academic career. These experiences have given me valuable insight into how large-scale computational problems can be tackled by applying statistical methods under tight deadlines while still maintaining quality control over your results. 

In addition, I have taken classes such as AI/ML Systems Design & Implementation and Machine Learning Algorithms. These classes have helped me develop new ways of approaching problems while also providing an understanding of why certain algorithms work better than others when applied in specific situations. 

I am a Data Science Major at UC Berkeley and have been for two years. In order to graduate with a major in Data Science, you must complete four required classes, one of which is an independent study project. 

I have chosen to take this independent study project in order to gain hands-on experience with a data science problem of my choosing and to learn how to effectively apply machine learning algorithms in the real world. My goal is to create an application that can accurately predict where students need tutoring based on their past grades. This project will require me to use various classes of statistical models, such as regression, decision trees, and neural networks. 

How to Write a Personal Statement for Masters Programme in Data Science?

If you are looking for the best way to write a sample personal statement for a master in data science, you should follow these steps: 

• Step 1: The first step is to find out what courses are available in your area and how long it takes to complete them. You can find this information on websites online. 
• Step 2: Once you have this information, you need to think about how much time you will have available each day. It is important that you do not leave your studies until you finish all of your courses because once you finish your degree program, there will be no more work available for you. Your ability to continue working will depend upon how well your personal statement for data science courses was received by universities and whether or not they offer scholarships for those who want to study abroad or online. 
• Step 3: In order to write a good personal statement for M.Sc data science, you will need to think about why you want to continue your education after completing your bachelor's degree program. This could be because of what happened during college or because of something else entirely (such as family obligations). If it is something that happened during college, then you will need to explain what that event was and how it has affected your desire to continue your education. If it is something that happened outside of college, then you should talk about how that event impacted your academic performance and why you want to continue studying.

Data Science Personal Statement Example 

Following is a data science personal statement example. You can refer to this data science statement of purpose example and keep in mind the necessary points.

Source: personal-statement-examples.com

Tips to Write an Effective Data Science Personal Statement

The following tips will help you write an effective personal statement for a master in data science: 

1. Use a Template

It's best to use a template that has been created by experienced admissions officers and other professionals in the field. This means you can skip the writing process entirely since they've already done most of it for you. 

2. Keep Your Sentences Short and Simple

Your goal should be no more than one or two paragraphs per section (including your application summary), which means keeping your sentences as short as possible without compromising clarity or coherence. If there are too many adjectives or numbers used in an otherwise simple sentence, try replacing them with action verbs like "ran" instead of "ran fast." 

3. Avoid Clichés

In your data science personal statement sample, instead of saying things like "I am dynamic," try saying something more descriptive such as “I am highly dynamic” instead because this shows off how creative your mind works while also showing off how well-rounded personas are important traits needed by anyone working at companies when writing an M.Sc data science personal statement.  

Do’s and Don’ts While Writing Personal Statement

Data science is a booming field with a lot of opportunities. You can work anywhere and make a good salary with this skill. If you think that it’s not for you, then it’s time to think again. The world has changed and so have our needs as individuals. Data science professionals will be needed in the future because of their role in shaping our lives as we know them today. In order to pursue a career in this broad field of data science, it is recommended that you pursue KnowledgeHut to learn its principal aspects and gain in-depth knowledge about data science. Data Science Courses to learn its principal aspects and gain in-depth knowledge about data science.

Frequently Asked Questions (FAQs)

Find out what diploma courses are available in your area and how long it takes to complete them. Once you have this information, you need to think about how much time you will have available each day. After evaluating all these things, start writing your personal statement using templates. 

• The reason(s) why you selected this subject(s) 
• Your chosen area of study and how it relates to the current studies 
• Your experiences in relation to your chosen subject(s) 
• What are your interests and responsibilities in relation to the subject you are studying? 
• After university, what's next? 
• A summary of why you will make an excellent student 
• Don’t use quotes 
• Don’t let spelling and grammatical errors spoil your statement. 
• Don’t copy and paste 

During the writing of the letter of intent for the MS in Data Science course, it is important to take into account the basic questions asked by the institution, including what kind of ambitions the prospective candidate has and the inspiration behind those ambitions. If the students do not want to sound conversational in their essays, then they should keep in mind that the tone should be formal instead of informal.

Ashish Gulati

Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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Upcoming Data Science Batches & Dates

Data Scientist CV Example for 2024 (Skills & Templates)

Create a standout data scientist cv with our online platform. browse professional templates for all levels and specialties. land your dream role today.

Welcome to our Data Scientist CV Example article. In this article, we provide a detailed explanation of how to write a comprehensive CV for a data scientist role. We discuss the key skills and qualifications you should include, as well as helpful tips and tricks to make your CV stand out from the competition. We also provide a downloadable Data Scientist CV template that you can use as a reference to write your own CV. With this information, you can create a powerful CV that will give you the best chance of landing your dream job.

We will cover:

• How to write a CV , no matter your industry or job title.
• What to put on a CV to stand out.
• The top skills employers from every industry want to see.
• How to build a CV fast with our professional CV Builder .
• What a CV template is, and why you should use it.

What does a Data Scientist do?

Data Scientists use data to uncover insights and trends that can be used to inform business decisions and strategies. They use sophisticated tools and techniques, such as machine learning and statistical analysis, to analyze large datasets and uncover patterns and correlations. They also build predictive models to forecast future trends and events. Data Scientists use their findings to help businesses make better decisions, improve their products and services, and increase their overall profitability.

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What are some responsibilities of a Data Scientist?

• Cleaning and preparing data for analysis
• Analyzing data to identify patterns and trends
• Building models to predict outcomes
• Visualizing data and communicating findings to stakeholders
• Developing machine learning algorithms
• Researching and staying up-to-date on the latest technology and trends
• Writing code to automate data collection and analysis
• Managing and optimizing databases

Sample Data Scientist CV for Inspiration

Name: John Doe

Address: 123 Main Street, New York, NY 12345

Phone: (123) 456-7890

Email: [email protected]

John Doe is a highly motivated and experienced Data Scientist with a track record of success in leveraging data to solve real-world problems. John has a deep understanding of data mining, machine learning, statistical modeling, and big data technologies and has a unique ability to communicate complex technical concepts to stakeholders of all levels. John is passionate about helping organizations make better informed decisions through data.

Work Experience:

• Developed and deployed a machine learning model to predict customer churn rates with 95% accuracy.
• Identified key customer segments and developed a marketing strategy to increase customer loyalty.
• Created and maintained dashboards for executive team to monitor performance across multiple KPIs.
• Developed predictive models to identify potential risk factors in the credit scoring process.
• Analyzed customer data to develop strategies to increase customer engagement and retention.
• Conducted market research to identify potential new customer segments.
• M.S. in Data Science , Cornell University, Ithaca, NY - May 2017
• B.S. in Computer Science , Stanford University, Palo Alto, CA - June 2015
• Data Mining
• Machine Learning
• Statistical Modeling
• Big Data Technologies
• Data Visualization

Certifications:

• Certified Data Science Professional (CDSP)
• Certified Big Data Professional (CBDP)

Languages: English, Spanish

CV tips for Data Scientist

Crafting an impeccable CV that kickstarts your career is a challenging endeavor. While adhering to fundamental writing principles is beneficial, seeking guidance customized for your unique job pursuit is equally prudent. As a newcomer to the professional realm, you require Data Scientist CV pointers. We've curated top-notch advice from experienced Data Scientist individuals. Explore their insights to streamline your writing journey and enhance the likelihood of fashioning a CV that captivates potential employers' attention.

• Highlight your technical skills that are relevant to the job position.
• Include specific accomplishments that prove your capabilities.
• Describe your work experience in a concise and clear format.
• Include relevant education and certifications.
• Include links to your portfolio or online projects.

Data Scientist CV Summary Examples

A Data Scientist CV Summary or CV Objective is an important tool for job seekers in the field of Data Science. It provides employers with an overview of the applicant's expertise, experience, and qualifications, enabling them to quickly determine if the applicant is a good fit for the position. By using a CV Summary or CV Objective, applicants can showcase their unique skills and strengths in a concise manner, and ensure that they stand out from other applicants. For Example:

• Proficient Data Scientist with 5+ years of experience in building data models and analyzing large datasets to identify trends and patterns.
• Utilizing advanced statistical and machine learning techniques to develop predictive models and uncover actionable insights.
• Proficient in Python, R, SQL, and Tableau for data analysis and visualization.
• Experienced in working with different stakeholders to interpret and communicate data-driven findings.
• Strong problem-solving skills and an eye for detail to identify and solve complex data issues.

Build a Strong Experience Section for Your Data Scientist CV

A strong experience section on a data scientist CV is important for highlighting the data scientist’s unique skills and accomplishments. It should include concrete examples of the data scientist’s achievements, technical capabilities, and any relevant certifications. This will demonstrate the data scientist’s expertise and help them stand out from other candidates. Additionally, the experience section can be used to demonstrate knowledge of industry-specific tools and software, and it can be tailored to the specific job the data scientist is applying for. Overall, a strong experience section can be a highly effective way of showcasing a data scientist’s qualifications and capabilities. For Example:

• Designed and developed big data analysis solutions using Hadoop, MapReduce and Apache Spark.
• Analyzed and interpreted large datasets to develop predictive models and uncover trends and patterns.
• Developed scripts and programs to automate data analysis processes and improve accuracy.
• Created data visualizations using Tableau and other tools to facilitate data-driven decision making.
• Collaborated with cross-functional teams to build data-driven solutions to business problems.
• Led presentations to upper management with insights and recommendations based on data analysis.
• Implemented machine learning algorithms to build predictive models for data-driven insights.
• Developed and maintained data pipelines to ingest, clean, and transform large datasets.
• Built data warehouses to store structured and unstructured data from multiple sources.
• Optimized data architecture to improve query performance and scalability.

Data Scientist CV education example

A Data Scientist typically needs a combination of strong technical and analytical skills. A bachelor's degree in a quantitative field such as mathematics, computer science, economics, or statistics is often required. A master's degree or PhD in a similar field is often preferred. Additionally, Data Scientists should have experience with programming languages such as Python, R, Java, and SQL. Knowledge of data mining, machine learning, data visualization, and other related topics is also highly desired. Here is an example of an experience listing suitable for a Data Scientist CV:

• Masters in Computer Science, XYZ University, 2019
• Bachelor of Science in Mathematics, ABC University, 2017
• Certificate in Data Science, DEF Academy, 2020

Data Scientist Skills for a CV

It is important to add skills for a Data Scientist CV because employers will use this information to determine if you are a good fit for the job. By including the most relevant and up-to-date skills, you can demonstrate to employers that you have the technical and analytical know-how to effectively perform the job duties. It also helps to highlight your strengths and showcase why your background makes you a qualified candidate for the position. Soft Skills:

• Critical Thinking
• Problem Solving
• Communication
• Collaboration
• Interpersonal
• Adaptability
• Organizational
• Time Management
• Data Analysis
• R/Python Programming
• Data Warehousing
• Database Management
• Data Cleaning

Common Mistakes to Avoid When Writing a Data Scientist CV

In today's competitive job market, an average of 180 applications floods employers' inboxes for each vacant position. To streamline this influx of CVs, companies frequently employ automated applicant tracking systems that weed out less qualified candidates. If your CV manages to surpass these digital gatekeepers, it must still captivate the attention of the recruiter or hiring manager. Given the sheer volume of applications, a mere 5 seconds is typically allocated to each CV before a decision is reached. With this in mind, it's crucial to eliminate any extraneous information that might relegate your application to the discard pile. To ensure your CV shines, consult the list below for elements to avoid including in your job application.

• Skipping the cover letter: A well-crafted cover letter is an opportunity to showcase your suitability for the role and express your enthusiasm for it.
• Excessive jargon: CVs laden with technical terms can alienate hiring managers who lack specialized knowledge.
• Neglecting vital details: Incorporate your contact information, education, work history, and pertinent skills and experiences.
• Relying on generic templates: Tailoring your CV to the specific job exhibits your commitment to the position and company.
• Errors in spelling and grammar: Proofreading is essential to eliminate typos, spelling errors, and grammatical blunders.
• Overemphasizing duties: Highlight accomplishments to underline your candidacy's value.
• Sharing personal information: Steer clear of revealing personal details like age, marital status, or religious affiliations.

Key takeaways for a Data Scientist CV

• Highlight technical skills and knowledge, including programming languages, modeling techniques, and databases.
• Include relevant work experience, such as projects you have completed, or data analysis you have performed.
• Include any education in data science, computer science, mathematics, or other related fields.
• Demonstrate strong communication and collaboration skills, as well as an ability to work in a team environment.
• Highlight any achievements or awards you have won.
• Include any volunteer experience in data science or relevant fields.
• Detail any certifications or courses you have completed.