How to Write a Data Scientist Resume That Gets You the Job

Anthony PellegrinoAnthony PellegrinoLast updated

Trying to write a data science resume that stands out to your recruiter and hiring manager?

Doing this may seem like a monumental feat. That's especially true if you've never applied to a data science job before!

In this article, we'll guide you step-by-step and section-by-section to write a great data scientist resume.

With over 2 million data science jobs in the US, the field of data science continues to grow. But with competitive salaries and competitive benefits, data scientist roles are increasingly hard to get.

This guide will help you:

  • Write a junior or senior data scientist resume that gets you more interviews.
  • Learn how to describe your skills that maximizes their impact.
  • Highlight your most relevant projects that align with your new role's goals.

Let's get started!

Want to skip straight to the resume templates?

Table of Contents:

What to Include in a Data Scientist Resume

Like most resumes, your data science resume should only include a few sections:

  • Contact Info: This is your basic contact information and includes your name, location, email address, and
  • Education: Your education can either go first or last on your resume depending on your experience. If you're going after your first job, your data science degree or education will likely live at the top of your resume. If you've worked for a number of years, your education may be at the bottom of your resume.
  • Relevant Experience: Highlight the companies you've worked for and projects you've completed.
  • Skills: What additional data science skills do you that can help you stand out? What languages do you use? Have you built side projects in Java? Are you learning a new R framework?

How to Format a Data Scientist Resume

Abstract by Oleg Shcherba

The format of your resume is just as important, if not more important, than the content of the resume.

This may sound silly, but think about it: if your resume isn't formatted well then it doesn't matter what it contains. Recruiters will simply toss it out before they can evaluate your qualifications.

Companies increasingly use Applicant Tracking System (ATS) to help screen resumes. ATS is a kind of automated software that scans your resumes for keywords.

If your resume is not formatted in an ATS-friendly way, it could very well be that no hiring manager ever gets to see it, even if you're qualified for the job.

Nevertheless, this can easily be avoided using a data scientist resume template.

Use the "reverse-chronological" format when listing your relevant experience.

Chances are you are already writing your data scientist resumes this way.

"Reverse-chronological" order simply means that your most recent work experience is listed at the top.

Then, your first job should be the last bullet point in your relevant experience section.

How to Write A Data Science Resume

Section by Section

The Data Scientist Resume Header

Abstract by Marina Mogulska

The first part of your data scientist resume is the header.

Here is where you need to list all your basic and contact information. Be sure to include the following personal information in your header:

Your Name: As obvious as it sounds, it is still important to point out. Be sure to include your name in a slightly larger font than the other text in the header. This way, it can be easily spotted and differentiated from the rest.

Remember that great data science resumes are easily scannable by those reviewing them.

Your Address: Be sure to remember your address in your header, preferably close to your name. It's typically a good idea to include your full address if you're applying for a job in-person. If you're applying to remote roles, your current city will suffice.

Social Media: Your public social media channels may be a less obvious inclusion than your name or address. Believe it or not, this may be preferred by some companies.

Including your social media can really make you stand out as a data science candidate. This is especially true if you have social media that shows off some of your past work.

At the very least, it's a good idea to include your LinkedIn profile, a Github account if you have one, and your personal website.

If you're working for a media agency or in a role that's heavily aligned with marketing, relevant social media channels can help you stand out.

Professional Summary: Finally, you could add a data scientist resume summary in your resume header if you so choose. Nevertheless, this is typically not recommended unless you are a data scientist with a long work history (10+ years).

If you decide to include a professional summary in your data science resume, just remember to be short and sweet. Professional summaries should be concise while clearly communicating why you would make a good fit for the particular role as a data scientist.

As the name suggests, it should also summarize your professional data science experience.

An example of a well-written professional summary for a senior data scientist:

Apple and LinkedIn data scientist with 11 years of experience helping businesses maximize outcomes. I strive to grow a team of data professionals and help them achieve their potential. I'm experienced in creating data models using R and Python to predict changes in user behavior. I've helped decrease customer churn by 37% in my current role and built a data-dashboard using machine learning and a JavaScript front-end to better visualize growth in real time.

An example of a poorly-written professional summary:

I've got 11 years of experience as a data scientist. I'm looking for a new role that lets me work from home. I graduated with a Masters in CompSci from UCLA.

What about if you're a junior data scientist looking to get your first or second job after college?

Your professional summary will likely look different. You're trying to join a growing team or be part of a larger data team that will help coach you into bigger and better skills.

Your data scientist resume summary is meant to highlight specific skills that fit the job description. This example skips over a great opportunity to show off what you do best.

An example of a well-written junior data scientist objective:

I'm a junior data scientist with 2 years of experience at a data analytics firm where I use Python to build pricing models for SaaS products. At NYU, I studied machine learning and graduated Cum Laude with a degree in computer science. I also led our school's statistics club and built a university dashboard with JavaScript to track our meetups.

This example does a few things. If you're a junior, it shows that you spent time in school studying data and thinking about your career. You studied hard (graduated Cum Laude) and worked on side projects.

Even if you don't have work experience, show a recruiter that you're serious about your role and career goals. Data scientists boast a wide range of experience, so help your recruiter understand your qualifications clearly and quickly.

If you spent a summer studying machine learning for fun or are interested in the data science field because a family member is too, you can highlight a bit of your personality as well.

Relevant Experience for Data Scientist Resumes

The most important part of your data science resume is the relevant experience section. Any data science resume format you find will have the relevant experience front and center.

As we mentioned, it's common practice to list your previous jobs in reverse-chronological order. Therefore, your most recent and most high-level experience should be at the top, easily accessible at first glance.

Remember to include the basic information from your past data science positions. Each listing should consist of a job title, company name, location, and when you held the position.

Then, of course, list the various bullet points (with plenty of action words) that describe your role and your particular impact during it.

It's best to demonstrate your accomplishments more than the simple responsibilities you had in your previous positions.

Your data science hiring managers will likely have a good idea about the scope of any data scientist or data analyst position.

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They are more interested in the measurable impacts you had in your previous roles. We'll give you a couple of examples of solid and weak data science resume job descriptions below to help give you a better idea.

Suppose you have many years of previous data science experience. In that case, you probably don't want (or need) to list every single previous role.

It's typically best to keep the relevant experience section limited to the past five years.

However, this is not necessarily a hard rule. If you think that suitable positions from the past 6 - 8 years would be a pertinent addition, go ahead and include them.

Just be sure that it doesn't extend your resume over a single page. Even if you're a veteran data scientist with an impressive background, a one page resume is enough. Your resume will only act as a reference.

You can use your data science interview to shine a light on all the projects you've done, past and present!

Strong Relevant Experience Description for a Data Scientist:

Apple (Data Scientist) 2015-2018

  • Built a customer attrition random forest model that improved monthly retention on the App Store. Recurring subscription purchases increased by 12 basis points for apps featured at the top of search.
  • Coordinated with the product and marketing teams to determine what kind of App Store interactions resulted in maximized subscription opt-ins, increasing conversions by 18%.

Weak Relevant Experience Description for a Data Scientist:

  • Worked with product managers to perform cohort analysis.
  • Constructed operational reporting in Tableau to improve scheduling.
  • Implemented a long-term pricing experiment to enhance value.

Data Science Resume Education Section

Abstract by Natasha Remarchuk

Suppose you've read some of our other posts on how to write a great product manager resume or seen Stephen's video about product management resumes.

You'll find that we've said that the education section of the resume is not necessarily as important as the relevant experience.

While this is true, it must be said that data scientist positions may be more demanding in terms of their educational qualifications.

Machine learning, data analytics, and data mining are in-depth fields that can't be completely explored with a Udemy course. Your data scientist degree gives you a chance to show off your deeper technical skills.

It is not uncommon for some data scientist job listings (especially senior ones) to prefer Master's degrees or PhDs in Computer Science.

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The education section should not take up that much space on your resume.

It shouldn't be nearly as big as the relevant experience section, as that is still the most critical part of your resume.

Unless you are a recent data scientist graduate or junior data scientist with little previous experience, your education section should be listed below your relevant experience section.

You must include the pertinent information in this section, but remember to keep it brief. Your education section on your data scientist resume should only consist of:

  • Your Degree(s),
  • The Name(s) of the University,
  • Your Date of Attendance and Graduation.

If you have 3+ years of relevant experience, it's best to keep this education section limited to just this information.

However, suppose you're a recent graduate.

Then, you can include some additional information if it demonstrates you're a good fit for the role. For instance, outstanding educational achievements pertinent to data analysis, such as past internships or data scientist awards, could be an appropriate addition.

Your school may have also have alumni networks to connect with other data scientist professionals.

Data Scientist Education Example

MA in Computer Science, UCLA (2013-2018)

  • 3.9 GPA in machine learning and Java coursework
  • Built a predictive model for upcoming student admissions for the university to anticipate how many dorms needed to be built. Used data mining to make decision trees and regression modeling.
  • Created a NLP application with classmates for my senior project that used AI to predict text input in a food delivery app.

This is a strong education example. It highlights your successful academic career and focuses on projects completed. It shows relevant skills and a desire to improve yourself outside of coursework.

The Skills Section of the Data Science Resume

Last but not least comes the Skills section. Like many technical roles, this part of the resume does hold significant weight.

It is possible for some data science roles to be very specialized or require particular technical skills. Therefore, include as many relevant skills as possible in your skills section.

A good rule of thumb is to look at the necessary qualifications listed in the job posting. The job postings will list the required and preferred skills needed for the role in question.

The most common skills that could be included on a data scientist resume are:

  • Python Data Analysis Frameworks (NumPy, Pandas, Scikit-Learn, Keras)
  • SQL, MySQL
  • Git
  • Supervised Learning (linear/logistic regressions, decision trees)
  • Unsupervised Learning (k-means clustering)
  • Data Visualization (Tableau, Excel)
  • Customer Segmentation
  • Productionizing Models
  • Recommendation Engines
  • Propensity Modeling
  • Data Regression models

You should list your most essential technical skills first moving down. A hiring manager will think that you list your skills in chronological order with how comfortable you are with them.

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It's best to exclude any skills that don't pertain directly to the role or that you're not necessarily that comfortable with. Like if you spent a summer trying to build an app in C++ only to give up and go back to Python, don't say you're fluent in C++!

The Importance of Honesty

It's important to remain honest in this section.

Hiring managers understand that it's an easy section to exaggerate, and a seemingly dishonest candidate is dead in the water.

It's easy to try and cram your data scientist resume with as many keywords as you possibly can to try and make it to the next round. You just need to get into the interview, right?

By keeping your data scientist resume targeted and concise, you actually have a much better chance of standing out. Your data science skills can have room to breathe when not everything seems like it carries equal weight.

Data scientists are expected to do deep dives into the inter workings of a business. It'll be obvious right away if your data scientist resume was fudged. It'll be even more obvious when you get called into a data visualization project and don't know where to start!

If you need more experience to help your resume stand out, consider starting with a junior data scientist resume format. Show that you're willing to learn and grow into the skills in a job description.

Being honest and open about your current skills goes farther than fudging the truth.

Tips & Tricks for Writing Your Best Data Science Resume

Abstract by Oleg Shcherba

Keep Data Science Resumes Brief

A great data science resume cannot be great if it is not brief and concise.

You should be firm on keeping your resume to a single page.

Read that again. Even if you're writing a senior data scientist resume, you should keep everything on a single page.

Your interview is where you can deep-dive into your previous roles or even that Scala project you worked on.

As harsh as it sounds, a hiring manager may throw your resume away without glancing at it if it runs longer than a single page. They don't need to know about every time you migrated to a SQL database.

Recruiters nowadays need to review hundreds, if not thousands, of resumes, especially at the Big Tech companies. That's especially true now that data scientists are in high demand.

Most simply do not have time to evaluate lengthy resumes.

Tailor Your Resume to Data Science Job Postings

Many aspiring data scientists may simply make one resume and send it out to various jobs.

It's certainly possible to receive many job offers this way. Still, it's much more effective to take the extra time and tailor your resume to the specific data science jobs.

The reason for this is simple. More often than not, the company you're applying to is using ATS software.

ATS works like a search engine web crawler, scanning texts for particular keywords.

If you tailor your data science resume to the keywords found in the job listing, you significantly boost your chances of making it past this software.

But it doesn't just stop there. Once your resume makes it past ATS, a human will inevitably review it. They, too, will notice that your resume matches the keywords found in the job description, which communicates that you're a good fit and pay attention to the details.

Given how little time recruiters will spend evaluating your resume, it's generally a good idea to take the extra time to do this. It will quickly impress the recruiter, bringing your resume to the top of the list.

There may also be unconscious bias by a recruiter or hiring manager. If they've seen a data science resume in the past that they used to hire someone, they may be looking for something similar unknowingly.

Bonus Tip: Be sure to use a professional email address when sending your resume. "[email protected]" may be your first email address. But try to use a .edu or more professional sounding handle if you can. "[email protected]" will go a lot farther than the email address you registered in middle school. 

Demonstrate Data Scientist Skills If Lacking In Job Experience

What do you do if you don't have that much previous data science experience?

You may be wondering if it's possible to still land a job like the data scientists you aspire to be like. The answer is yes, so long as you can demonstrate the necessary data scientist skills in another way.

Perhaps you're a new grad and only have a junior data scientist resume on your hard drive from a resume-writing class you took.

Like other tech roles, such as software development or engineering, it is possible to create your own experience, if you will.

You can work on your own data science projects that can demonstrate your skills on the resume.

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You can get your start as a freelancer, perhaps. Open-source data science projects are another excellent option for building more demonstrable experiences.

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At the end of the day, a lack of professional experience, especially as a new grad, should not keep you from pursuing your career goals as a data scientist.

Instead, these are just ways you could build relevant experience on a sparse data scientist resume.

You can also focus on soft skills you may have that show your willingness to learn. Are you willing to go the extra mile with classes or courses in your off-hours?

Junior Data Scientist Resume Template

Below, you'll find a downloadable PDF resume template for a junior data scientist who is looking for their first full-time job after graduation.

It highlights work as a data analyst and some machine learning side-projects the candidate has worked on (remember, those are important!)

This data scientist resume example will help you get started building out your own. Remember to tailor your resume to match the job description you're applying for.

More Data Scientist Interview Resources

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Worried about data scientist interview questions? Practice common data science questions in this course.

Start Learning

If all goes well, your data science resume will soon result in calls from recruiters to schedule interviews. While we hope this article helps you get your foot in the door, you'll ultimately need to ace your interview to get the offer.

Check out our Data Scientist Interview resources to help you do just that:

💬 Review more commonly asked data science interview questions.

📖 Read through our company-specific Data Scientist interview guides

👯‍♂️ Practice your behavioral and leadership skills with our mock interview practice tool.

👨‍🎓 Take our complete Data Scientist interview course.

Learn everything you need to ace your data science interviews.

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