7 Data Scientist Resume Examples for 2022

Author: Stephen Greet, Co-founder
Published on: April 12, 2022

Since starting BeamJobs, we've reviewed countless data scientist resumes, and we've made a concerted effort to distill what works and what doesn't about each of them.

The number one tip to create an effective data science resume is to quantify your impact on the business! As a data scientist, you need to demonstrate you know how to focus on the metrics that matter to a company.

The seven data scientist resume examples below and our data scientist cover letter samples we give can help you build a great job application in 2022, 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 experience, you'll find what you need with our resume examples and templates, like our new Word resume examples (free).

Data Scientist Resume Sample

Why this resume works

  • Quantify your impact! You need to demonstrate to a hiring manager that, as a data scientist, you have materially impacted the companies you've worked for in the past.
    • This means you should quantify your value in terms of business impact, not model performance. Model performance metrics without context don't convey much.
  • The metrics you convey on your data scientist resume can be rough estimates. You just have to show that you deserve the first-round interview by convincing the hiring manager you will positively impact their team or company.

Entry-Level Data Scientist Resume Sample

Why this resume works

  • You can edit your current entry-level data scientist resume in our resume checker or build from scratch with our free resume builder tool.
  • If you include projects, demonstrate what tools were used and the results of those projects.
  • Be sure to discuss your projects in terms of their quantitative impact.
  • You can demonstrate the punch of any listed projects by framing a question and then answering that question with data.
    • Again, your results should be consistently stated in numbers. Even if the result is as silly as saving 18 minutes in selecting minutes as per the "Movie Recommendation Engine" project, it recognizes the importance of measuring impact.
  • As an entry-level candidate, include all relevant courses from college (or any boot camps).
  • If you opt to include a resume objective, customize it to the job to which you're applying by mentioning the target business by name and including relevant keywords from the job description. This is a great way to catch the hiring manager's eye.
  • Your resume skills section should be elaborate and impressive but not overwhelming or unbelievable.

Senior Data Scientist Resume Sample

Why this resume works

  • No matter what bullet point you read, the emphasis should be focused on positively impacting the business.
  • Your senior data scientist resume should show a clear career progression from data analyst to data scientist to senior data scientist.
  • Work experience bullets should highlight modeling techniques and tools used to implement those techniques in the context they were used. This demonstrates stronger command rather than just listing tools in the skills section.
  • If you have over four years of experience, it's appropriate that your work experience accounts for about 70 percent of the real estate on the page.
  • Should you boast 10+ years of experience, a resume summary works best. 
    • It conveys a snapshot of your career information in two to three sentences while allowing room for everything else to fit on one page.
  • The skills section should not be a laundry list. Instead, list your best skills thoughtfully and concisely.

Data Science Manager Resume Sample

Why this resume works

  • When seeking a "Data Science Manager" role, it makes sense that the projects for any "Lead Data Scientist" experience be focused on leadership and project ownership.
  • Again, the results of your projects 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. Explore our resume templates for 2022 to suit your specific needs; additionally, we've got 10 fresh and free resume templates for download that we created in Google Docs.
  • If you have many years of experience for each of your skills in the "Skills" section, hammer home that expertise by mentioning your years working with specifics like Python and SQL.
  • Don't be shy! As a seasoned data scientist, your work experience should take up most of the overall page space.

NLP Data Scientist Resume Sample

Why this resume works

  • When you're applying for a more specialized data scientist role like an NLP data scientist, it's important you demonstrate your proficiency in operationalizing models to have a big impact on the business.
  • Remember, the goal of any data scientist (including NLP experts) is to deploy models to make positive impacts on product and user experience. 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 past roles.

Data Scientist, Analytics Resume Sample

Data Scientist, Analytics Resume Example

Why this resume works

The first thing recruiters/hiring managers will notice when reading your resume is whether the fundamental qualifications to perform the job adequately are present.

    • Your data scientist, analytics resume should be formulated specifically to target the list of requirements that companies in the state of California commonly request. 
      • For example, 18 out of 20 job descriptions for data science, analytics in the state of California are Python, SQL, R, Tableau, and Hadoop (in that order).
      • After listing job-market-specific data, our free resume checker can assess your resume for industry best practices, spelling, and grammar. 
    • As tempting as personal projects are to showcase current skills, that is unnecessary if you have a proven history of dedication and experience at multiple companies with varying levels of expertise.

Metadata Scientist Resume Sample

Metadata Scientist Resume Example

Why this resume works

Entry-level metadata scientist roles can be difficult to land. However, your metadata scientist resume can be an excellent way to display large-scale data-manipulation skills required by the industry.

  • 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.
  • Show that you have tangible entry-level experience.
    • Though related work experience is preferred on your resume, entry-level candidates often share a common denominator: a light or nonexistent job history in the field.
    • Solve this problem with projects.
      • If you've worked on excellent projects that utilized and showcased the necessary skills required for the job, list them and watch your resume bloom with confidence!

Guide to Perfecting Your Data Science Resume in 2022

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 2022 takes you section by section through your resume to ensure you get that first interview.

You can successfully choose a winning resume format in 2022 that will snag employers' attention. If you're short on time, here are the quick-hit summaries of each section that you can easily apply to your resume:

  • Projects & Work Experience
    • 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.
  • Summary/ Headline/ Objective
    • Make this the job title you're looking for (i.e., "data scientist"), and don't worry about a summary unless you're making a career change.
  • Skills
    • Only include technical skills that you'd be comfortable having to code with/in during an interview. Avoid a laundry list of different skills.
  • Education
    • 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.
  • Contact Info
    • 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.
  • General Formatting Tips
    • Try to keep it to one page. Keep your bullets brief. Triple check your grammar and spelling and then have someone else read it.
  • Customization for Each Job
    • 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. I 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
  • Demonstrate what you did 
    • 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

  • Increased revenue
    • Example: You developed a pricing algorithm that resulted in a $200k lift in annual revenue.
  • Improved retention or conversion rate
    • Example: You built a model to predict who would cancel their subscription and introduced an intervention to improve monthly retention from 90% to 93%.
  • Increased growth
    • Example: You built a marketing attribution model that helped the company focus on marketing channels that were working, resulting in 2,100 more users.
  • Improved engagement rate
    • Example: You ran an experiment across different product features, which resulted in a 25% increase in engagement rate.
  • Saved labor
    • 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.
  • Lift in consumer satisfaction
    • 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 much more readable. Which of these two ways to describe reporting is more compelling?

  • I used Python, SQL, and Tableau to conduct daily reporting for the business
  • Using Python, SQL, and Tableau, I 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 three sample resumes above, you'll notice that only the entry-level data scientist resume has 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, I wanted to know which data science job boards were best, so I pulled together some data, laid out my assumptions and methodology, and made my conclusions.

Data Science Projects for Resume

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 I'm a data analyst looking to apply to data scientist jobs, I would put "data scientist" under my name as my headline:

Data Science Resume Headline

Skills that pay the bills

The most common mistake I see on data science resumes (that I used to make on my resume) is what I 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 I recommend you use in determining whether to include a skill on your resume is this: if 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 will be able to find jobs looking for that skill set. 

I 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 I was first applying to jobs out of college, I realized about 20 applications in, I spelled my 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. I've seen exactly four data science resumes where the email address on the resume was incorrect.

Make sure your email address is appropriate. While I don't doubt the authenticity of your "" 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.
  • Use Grammarly or similar to check the spelling and grammar in your resume. Then do that two more times. Finally, have someone else review it just for spelling and grammar.
    • Don't give the person reviewing your resume a silly reason to put it in the "No" pile. Check your grammar and spelling.

Customization for each application

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

  • Ensure that for each language you have extensive experience in (Python and R, for example), you have separate resumes emphasizing specific projects in each language.
    • So in this example, I'll have one "Python" resume and one "R" resume depending on what the job is seeking.
  • Read the data scientist job description. Do any specific projects you worked on come to mind as you read it? If so, include those projects as bullet points on your resume. Here are some sample questions to help you think of specific projects to list for different jobs:
    • 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 I mean by including particular projects for different jobs. Let's say that the "Senior Data Scientist" from the resume at the top of the page is applying for the position below.


In the "Ideally, you'd have" section, they mention they want someone who has "Experience with ETL tools." You'll see on my resume that I have nothing about data engineering listed. In reality, I had a large role in building out data pipelines in my fictional role as a senior data scientist at EdTech Company.

So all I'd do is change that section of my 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." I 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 2022. 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 towards landing your next (or first) data science job. Now please, I 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.


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