beamjobs

5 Data Scientist Resume Examples For 2021

Author: Stephen Greet, Co-founder
Published on: January 28, 2021

Since starting BeamJobs we’ve reviewed north of 1,000 data scientist resumes and I’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 five resume examples below will help you get started building a great data science resume in 2021 no matter what stage of your career you're at.

Whether you're looking for your first job as an entry level data scientist or you're a veteran with 10+ years of experience, you'll find a resume to give you some inspiration.

Data Scientist Resume Sample

data-scientist-resume-example.png

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 impact in terms of business impact, not in terms of model performance. Model performance metrics without context don't really convey much.
  • The metrics you convey on your resume can be rough estimates. You just have to show that you deserve a first-round interview by convincing the hiring manager you will positively impact their team or company.

Entry Level Data Scientist Resume Example

Entry Level Data Scientist Resume Example

Why this resume works

  • You can edit this resume in the resume builder or download the entry level data scientist resume PDF.
  • The projects at “Marketing Science Company” clearly demonstrate what tools were used and the results of those projects.
  • All project results are clearly stated in terms of their quantitative impact.
  • The projects in the “Projects” section demonstrate the ability to frame a question and then answer that question with data.
    • Again, the results are 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 shows recognition of the importance of measuring impact.
  • All relevant courses from college (or a bootcamp) are included since this is an entry level role.
  • The resume skills section is elaborate but not overwhelming or unbelievable.

Senior Data Scientist Resume Example

Senior Data Scientist Resume Example

Why this resume works

  • No matter what bullet point you read, the emphasis is on having a positive impact on the business.
  • The resume shows a clear career progression from data analyst to data scientist to senior data scientist.
  • The bullets highlight modeling techniques and tools used to implement those techniques in the context in which they were used. This demonstrates stronger command relative to just listing tools in the skills section.
  • Since this data scientist has over four years of experience, it’s appropriate that descriptions of their work experience account for about 70% of the real estate on the page.
  • It conveys a lot of information while still fitting on one page.
  • The skills section is not a laundry list.
  • You can edit this resume in our builder or download the senior data scientist resume PDF.

Data Science Manager Resume Example

Data Science Manager Resume Example

Why this resume works

  • Since this person is looking for a “Data Science Manager” role, it makes sense that the projects for their “Lead Data Scientist” experience are focused on leadership and project ownership.
  • Again, the results of the projects are stated clearly in terms of concrete impact (are you sensing a theme?). 
  • By using a two column layout for the resume more information can fit on a single page. Explore our resume templates for 2021 to suit your specific needs. Even with 9+ years of experience, it’s always ideal to try to keep your resume to one page.
  • The years of experience for each of the skills in the “Skills” section is a nice touch to really hammer home expertise in Python and SQL.
  • Work experience takes up a huge majority of the overall page space which makes sense for this seasoned data scientist.
  • You can edit this resume in our builder or download the data science manager resume PDF.

NLP Data Scientist Resume

nlp-data-scientist-resume.png

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 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 to product and user experience. Don't focus on the really technical aspects of the models you've built (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.

Guide To Perfecting Your Data Science Resume In 2021

Recruiters only spend an average of 7.4 seconds reviewing your resume so it’s vitally important that you catch their attention in that time. Our guide for 2021 takes you section by section through your resume to make sure you get that first interview.

You can successfully choose a winning resume format in 2021 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
    • What you’ve worked on, whether for a company or for yourself, should be the focus of your resume. Always try to include a measurable impact of your work.
  • Summary/ Headline/ Objective
    • Make this the title of the job 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. Don’t have a laundry list of different skills.
  • Education
    • Include relevant courses if you are looking for an entry level role. Otherwise, make your work the focus of your resume. If you went to a bootcamp, 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, zip is 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 check it.
  • Customization for Each Job
    • Read the 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.

Your goal when talking about your past work (whether that’s for another employer or on a side project) is to convince the person currently 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 past experiences as a data scientist is:

  1. Clearly state the goal of the project
  2. Demonstrate what you did 
    1. You can mention the programming languages you used, the libraries, modeling techniques, data sources, etc..
  3. 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 talk about your projects in a quantitative way:

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, they’re convincing, and they make your resume much more readable. Tell me, 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 saves 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. You’ll notice in the three sample resumes above 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 at 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 are 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 absolutely include their projects on their resume. Try to share anything about your projects that is available. Can you share the Github link? Do you have a link to a write-up you did about your project?

For entry level data science projects the more initiative you can show the better. Do you have any questions you’ve always wanted the answer to? 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 in 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're going to take the time to customize it for each role you apply to.

The only time you may want to include a summary or objective is 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 if you include it, so take the time to personalize it to each job.

Instead of putting an objective or summary on your resume, make sure to 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 that no one person could have expertise in. This is a big red flag to hiring managers. A quick rule of thumb: if the skills section is taking up a third of the page, it's taking up too much space.

The reason people makes 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 mention 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 on 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 there are 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 a lot 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 specific 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 in the context of 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.

undefined

Education

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 that are relative to data science to demonstrate you have a strong foundation.

Classes in subjects like linear algebra, calculus, probability, statistics in addition to 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 really not necessary to include your GPA.

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

undefined

Contact Information

The takeaway from this section is simple: this is not where you should make a mistake. Story time! When I was first applying to jobs out of college I realized about 20 applications in that I spelled my name “Stepen” instead of “Stephen”. Don’t pull a Stepen.

I’ve seen exactly 4 data science resumes where the email address on the resume was incorrect. Data suggests that when your email is wrong your response rate from companies drops to 0%. That’s just math.

Make sure your email address is appropriate. While I don’t doubt the authenticity of your “officefan4life@gmail.com” 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 analysis you do for Dungeons & Dragons? Active on Github or an open source project? Include a link to anything that is relevant to data and 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. As such, 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, make sure you 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. Then have someone else review it just for spelling and grammar.
    • Don’t give the person reviewing your resume a silly reason to put your resume 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 your resume for each job:

  1. Make sure that for each language you have extensive experience in (Python and R for example), you have separate resumes emphasizing specific projects in each language.
    1. So in this example, I’ll have one “Python” resume and one “R” resume depending on what the job is looking for.
  2. Read the job description. As you read it, do any specific projects you worked on come to mind? If so, include those projects as bullet points on your resume. Here are some sample questions to help you think of specific projects to include for different jobs:
    1. For example, if you have experience with attribution modeling and this is a marketing data science role then you should include that experience.
    1. Do you have experience with a certain library or modeling technique they mention? 
    2. Do you have experience in the domain of the specific job?
    3. Do you have any relevant industry experience to the company?

Let’s walk through a specific example to highlight what I mean for including specific 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 job below.

undefined

In the “Ideally you’d have" section they mention they want someone who has “Experience with ETL tools”. Well 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 resume: Worked closely with the product team to build a production recommendation engine in Python that improved the average length on page for users and resulted in $325k in incremental annual revenue

Customized for 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 are 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 in 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 they can to weed people out.

Conclusion

There you have it. A compelling, easy to read data science resume built for 2021. 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 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 check 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.

384383

Ready to build your resume?

Our free online tool will walk you through creating a resume that stands out and gets you hired at a top tech company.

  • Beautiful templates with eye-catching designs
  • Data-driven tips to help you make the most of your experience
  • Step-by-step walkthrough so you know what to focus on
  • Built by Google engineers with years of hiring experience