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Data Scientist Resume Example & Writing Guide

Data scientists are endowed with analytical talents they can use to full advantage when customizing their resume for any dream job. This writing guide, backed by a data scientist resume example, offers tips and hacks to gain that winning edge.
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Data Scientist Resume Example & Writing Guide
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This is the era of the geek, so let your flag fly! If you love crunching numbers, count yourself lucky: The Harvard Business Review named data scientist as the sexiest job of the 21st century. In an explosive labor market where big dreams can come true, taking a deliberate, analytical approach to develop your data analyst resume is the best way to increase your odds or even get multiple job offers.

Resume.io can help you put it all together to interview-winning effect. Our job search resources include more than 350 occupation-specific resume examples paired with writing guides.

With a time investment of about 20 minutes, this guide — backed by a data scientist resume example you can customize for your own use —will take you through the process step by step. You’ll learn how to: 

  • Optimize your data science expertise to the fullest advantage as the number of opportunities in your field skyrocket.
  • Fit your resume to the correct framework and best-suited format.
  • Illuminate your strengths convincingly in each resume section: header, profile, employment history, education and skills.
  • Apply professional layout and design principles to ensure your resume is recruiter-friendly.

First, let’s investigate the data science industry.

What does a data scientist do?

Businesses are snapping up people with your skills in record numbers. What’s behind these seemingly infinite choices of positions? Why would Harvard call data science sexy?

The market for data science: harnessing the power of analytics

The field reaches into all our lives, almost every day and you are on the forefront of making that data meaningful.

Statistical insight

Big data and data analysis spending is forecast to have a compound annual growth rate of 13.2 percent from 2018 to 2022. That means that by 2022, revenue would reach $274.3 billion, IDC believes.

In our technology-driven world, companies are gathering large caches of information about human behavior, demographics, and any other category that can be quantified. That information is useless on its own. That’s where you come in. As a data scientist, you take that data, analyze and interpret it, so that it can be used to make decisions about everything from which color to make a product to where the biggest need for a government program lies. Data scientists work with unstructured data and their modeling techniques may include machine learning and deep learning protocols. 

Many industries capitalize on big data. Which ones are making the most of it right now? Here are the top verticals where Data Science Central believes data scientists can use their skills best and earn the highest salaries:

  1. Finance
  2. Healthcare
  3. Travel
  4. Energy
  5. Manufacturing
  6. Gaming
  7. Pharmaceuticals.

The GeeksforGeeks list adds even more potential employer sectors:

  1. Retail
  2. Medicine
  3. Banking and Finance
  4. Construction
  5. Transportation
  6. Communications, media, and entertainment
  7. Education
  8. Manufacturing and natural resources
  9. Government
  10. Energy and utilities
  11. Outsourcing

Each of these industries uses big data to inform decision-making and solve problems, but each has different needs and goals. With that variety, it will be in your best interest to focus your resume as specifically as you can to each of your areas of interest. 

Statistical insight

The average annual base salary for data scientists is $ $97,483, according to Payscale. The top 10% of earners make $136,000 and the bottom 10% earn $69,000..

You need to be able to explain in your resume what value you will bring to a business with your analyses. Businesses are looking for ways to streamline processes, save money, sell more, improve efficiency, get ahead of trends, identify and refine their target audience, recruit appropriate talent, and use quantifiable evidence to make and test decisions. You can do that, but first you have to sell yourself.

How to write a data scientist resume

What virtually all resumes have in common, no matter what the profession, is their barebones structure, as follows:

  • Header
  • Profile (sometimes called summary or personal statement)
  • Employment history
  • Education
  • Skills

Step by step, we’ll look at each of these essential elements shortly from a data scientist’s standpoint. But first, here are some general considerations.

Your resume should fit on a single page. The more experience and skills you have, the more selective you will need to be in determining the most directly relevant content for your data scientist resume. And, as we’ll continue emphasizing, your resume should always be customized to match the requirements of every different job you apply for.

ATS-proofing your resume

That brings us to the first big challenge for many job seekers — passing through the applicant tracking systems (ATS). But as a data scientist, you should have little trouble. These systems use algorithms to rank your resume based on employers’ requirements. Resumes that don’t rank high enough end up on the slush pile. 

There are hundreds of ATS programs on the market, so there is no single way to ensure you will beat one, but as a data scientist, you may have an advantage because you know how to analyze information. Your first step is to do just that. Look at each job description (researching the employer also helps a ton) and decide which listed skills and attributes are most important to that employer. Then, make sure you use those skills and attributes in your resume. 

Expert tip

Here are some facts to help you ATS-proof your resume:

  • ATS software searches for exact keywords and phrases from job descriptions.
  • Some systems weight rare keywords more highly — for instance, unique skills are more sought-after.
  • Not all ATS can read data in tables or headers and footers.
  • Use acronyms and spell out entire titles just in case the ATS is programmed for only one.

Choosing the best resume format for a data scientist

Unless your career path is taking a new turn or is geared to consulting roles as an independent contractor, the most commonly used chronological resume format should be suitable. If you’ve worked primarily as an employee, this is the simplest way to organize your achievements in bullet-point highlights below dated employer headings. It’s also what recruiters prefer.

Consider alternative resume formats if you want to emphasize specialized skills, clients or projects, rather than where you’ve worked. In that case, a functional or hybrid (combination) resume format might be more suitable. Later on, we'll discuss the reasons why some data scientists might prefer to showcase their achievements in a "projects" section instead of the conventional employment history.

Now that you have an overview, let’s jump into crafting your resume.

Resume header

Choose a resume header design that will catch the reader’s attention and make a lasting impression. It also makes the document look more inviting to read, while your name, occupation and contact information stand out. Strengthen the impact by making your resume and cover letter match visually with the same personally branded design elements.

Resume summary example: These sentences add up to more than an introduction

Your resume summary — or, if you prefer, your profile or personal statement — briefly tells recruiters who you are and what you find most important about your career thus far. In this short narrative — no more than three to five sentences — you have the opportunity to showcase your professional personality and attributes while highlighting one or two of your biggest successes. 

This is the place to put in adjectives that describe your workplace demeanor and explain why you are the best candidate for the job. If you are tempted to be modest, resist! You should be proud of your accomplishments, but steer clear of overstating. If you are looking for your first full-time job, use any projects you have completed either at university or because as a data geek you do this stuff for fun.

Expert tip

Each summary should reflect the job for which you are applying. You don’t have to rewrite a completely new version for every position, but be sure to integrate the keywords that your analysis found most important to give your ATS ranking a boost.

Although this section appears at the top of your resume, you may want to write it last. That may depend on whether laconic creative writing is among your strengths. After all, it is called a summary so it may help to have all the data in front of you first.

See the data scientist summary from our resume example below.

Data Scientist summary 

Accomplished Data Scientist with a passion for delivering valuable data through analytical functions and data retrieval methods. Committed to helping companies advance by developing strategic plans based on predictive modeling and findings. Proven track record of analyzing complex data sets and serving as a solid, reliable advisor.

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Employment history sample: mine your own data

The employment history is the meat of your data scientist resume: It gives recruiters the roadmap of your career progress. Think of this section in the same way you think of putting together chunks of data to form a big picture. Each job or data science project is a stepping stone to the next. 

The key idea here is to show recruiters what you have brought to each job, how you have grown, and what steps you are ready to take next. Many data scientists begin their careers as software developers, data engineers, business analyst or data analysts , so if you’re trying to make the leap to data scientist, be sure to show a clear pattern of growth in responsibility and knowledge. Begin by thinking about all the skills you need to be a successful data scientist. You can think of what Innoarchitech calls the four pillars of data science:

  1. Business: General knowledge of how businesses operate or distinct information about one or more industries
  2. Mathematics: Statistics and probability are important to data analysis
  3. Computer science: Software engineering and data architecture
  4. Communication: Ability to explain your methods, findings, and conclusions both orally and in writing.

The Harvard Business Review describes a data scientist as a combination of data hacker, analyst, communicator, and trusted advisor. Being expert in all these areas makes you a true unicorn.

Expert tip

Remember to name the “Three Vs” that you dealt with — volume, velocity, and variety — when describing your data projects. 

Go heavy on the details in this section. At the same time, avoid unnecessarily wordy sentences and cut down on “auxiliary grammar” that can be shortened. This is one way to demonstrate that you track your own achievements and growth. Use strong action verbs as you write. 

Here are some sample phrases to get you started:

  • Devise and apply models
  • Analyze and interpret data
  • Determine optimal data sets and variables
  • Gather large structured and unstructured data sets
  • Create visualizations to communicate findings
  • Discover solutions and opportunities

When you write your work experience descriptions, try to anticipate questions recruiters may ask you, and include some answers. Potential questions may include those from the following categories:

  • Basic: What is the difference between data analysis and data science?
  • Statistics: What are correlation and covariance?
  • Data analysis: Explain different types of sampling.
  • Machine learning: Explain decision tree algorithms.
  • Deep learning: What is reinforcement learning?

If you can organically incorporate answers to these questions, or examples of how you have used these ideas in your employment history descriptions, recruiters are more likely to grant you an interview.

Projects section

You may have completed many projects that were not related to your employment or you may have been working as an independent contractor. If either of those is the case for you, consider organizing your experience by projects. That way, you can offer recruiters more details about your biggest achievements . You should still follow the same guidelines in the employment history section:

  • Organize in reverse chronological order.
  • Show a pattern of growth.
  • Use details and data.
  • Describe with strong action verbs.
Expert tip

This format for organizing your work history can also help if you have any gaps in employment. These gaps are less obvious, and more explainable, if you have been working as a contractor or on a single-project basis. 

This section is also a great way to include open-source contributions and blogs or social media content that relates to your career.

Below is a data scientist employment history sample you can modify.

Data Scientist employment history sample

Data Scientist at Viacom, New York
June 2016 — September 2022

  • Effectively utilized statistical techniques to develop prototypes and scalable data analyses.
  • Performed code development and optimized systems to better advantage.
  • Worked in teams to define requirements and lead projects from start to finish, always on time and within budget..
  • Discovered new and better data sources to advance company efforts.
  • Became highly proficient in using analytical tools such as SAS, Matlab, and STATA.

Junior Data Scientist at Macy's, New York
July 2014 — May 2016

  • Used statistical techniques to answer important business questions, some of which had not been addressed before.
  • Worked closely with various teams to encourage constantly evolving experimental design and data analysis.
  • Created interactive visualizations to enhance decision-making capabilities throughout the company.
  • Gained experience using computing tools such as Hadoop, Map/Reduce, and Hive.
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Resume education example: let your STEM background work for you

Data science is a fairly new area, so you probably don’t have a degree in it, although certification programs and “bootcamps” are popping up to fill the void. If you have completed one, that’s great, but you can get a job as a data scientist without one. The most sort-after degrees for recruiters seeking data scientists are:

  • Mathematics
  • Computer science
  • Information technology
  • Physics
  • Statistics and, of course,
  • Data science

If you have a minor in any of these subjects, you should also include that when you list your education. Many data scientists have master’s degrees, which focus their field of expertise, but a master’s degree is not a requirement to get hired as a data scientist, especially if you have a background in the subjects above. If you want to strengthen your resume or fill in skill gaps, consider a certification program or further studies. Other STEM degrees, such as biotechnology or engineering, may also lead to a career as a data scientist. 

Below is the education section from our data scientist resume example.

Data Scientist education sample

Bachelor of Computer Science, Fordham University, New York
September 2010 — May 2014

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CV skills example: Aim for distinguishing abilities

The skills section of your CV is an at-a-glance list of the talents you have that match the job you are seeking. It should include from five to 10 of your top abilities.

Now’s the time to brainstorm a list of every program, statistical model, branch of math, etc. that you know. These are the hard skills you need to do your job. Next, think about your people skills and other soft skills such as communication, organization, and time management required to be a productive worker. Turn this into a master list that you can add to as you grow as a data scientist and search for new challenges.

Expert tip

This is the easiest section to individualize for each job, but it shouldn’t be the only one you alter. Make sure you consider each job description and target those specific requirements when you customize your resume.

This section is also a great place to add those rare keywords that will distinguish your resume and help you beat the ATS.  Skills can be thought of in these three categories:

  1. Necessary skills: These are the abilities you need at the very least to do the job. You may have used these skills in a lower-level position such as data analyst. They are general skills such as economics, statistics, and software development.
  2. Defining skills: These are the skills you will need to perform your job on a daily basis. Examples include data mining and analysis, machine learning, and predictive modeling.
  3. Distinguishing skills: These are advanced skills that elevate your resume in ATS rankings and impress recruiters. If you know classification algorithms, econometrics, or model building, great!

As you build your skills section, choose higher-level skills over lower-level skills. You don’t need to list any necessary skills if you have defining and distinguishing technical skills . Don’t neglect soft skills, however. As a data scientist your analytical ability extends to communicating your findings and making the case for your solutions. 

Check out the data scientist CV sample for the skills section below.

Data Scientist CV sample for the skills section 
  • Algebra Skills
  • Quantitative and Analytical Skills
  • Advanced Communication Skills
  • Troubleshooting Skills
  • Flexibility and Adaptability
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Resume layout and design: clarity over creativity

As a data scientist, you know that the message is more important than an ornate design. Use this philosophy to develop the look of your resume. This is a visual representation of your professional personality, so be sure to reflect that.

Expert tip

Check for typos, save your resume as a PDF file, and scan for formatting errors. Even tech-savvy professionals can slip up. Don’t be one of them!

The main idea is to keep your resume document legible. Recruiters will spend only seconds scanning for information and getting an impression of who you are. If your resume features big blocks of type or a confusing layout, it is likely to end up in the garbage. Here are a few rules of thumb:

  • Vary your line lengths.
  • Use bulleted lists instead of paragraphs.
  • Limit the amount and brightness of color.
  • Make sure your contact information, skills, and job titles are easy to find.
  • Avoid using tables or placing important information in headers and footers that the ATS likely can’t read.

Start with one of the field-tested resume templates from Resume.io's four style categories: Simple , Creative , Modern , or Professional . If you choose to modify the design elements or formatting in any way, make sure you don’t overdo the color or change the font to one that is harder to read.

Key takeaways for a data scientist resume

  1. Data science is a recent addition to the range of technology careers, and skilled data scientists are highly sought-after.
  2. You should have plenty of data to show the challenges you have faced, the actions you took, and the solutions you derived.
  3. Consider using a project section instead of employment history; it may be a better way to showcase your abilities.
  4. Focus your skills section on the rare abilities that will boost your ranking in the ATS and impress recruiters.
  5. Clarity and legibility are more important than creativity when it comes to your resume’s design

Use resume.io, the resume builder -tool, and its expertly designed templates to elevate your resume. Get started today!

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