September 28, 2022

The 8-Step Guide to the Perfect Data Science Resume (2022)

3cg-how-write-resume-data-science

If you want a job in data science, you’ll have to get past the “Gatekeeper,” better known as the recruiter or hiring manager. And that means writing a great resume. 

Here’s the hard truth: No matter how many technical skills you have in data science, your career simply won’t get off the ground without an impressive resume. 

It might be unfair that this single document can stand in the way of you and your dream career, but it’s the absolute truth! 

Fortunately, there are some tips and tricks to effective resume-writing. These can help you stand out amongst other data science applicants. 

Want to know what they are? Keep reading! 

In this post, we’ll reveal some little-known secrets about how to write a great data science resume. You’ll get access to expert testimonials from real hiring managers in the industry as well as some pro-tips for making your resume stand out. Along the way, we’ll be pointing out some crucial mistakes to avoid — the ones recruiters see as red flags!

Need relevant skills and knowledge to put on your resume right away? Check out our comprehensive data science courses now for free! 

Step #1: Keep Data Science Resumes to A Page or Less!

The challenge is to be both thorough and concise. A good resume should only be one page long (unless you have ten years of relevant experience for the job you’re applying to).

Even then, there are recruiters out there who will toss any resume longer than one page. Crazy? Maybe. But there’s a good reason for this. 

Hiring managers receive a lot of resumes every day. So, they only have about 30 seconds to look over each resume and make a decision.

“Let me be honest,” says Stephen Yu, president and chief consultant at data analytics consulting firm Willow Data Strategy. “Before I meet somebody, the time that I spend [on each resume] is less than 30 seconds. If that resume doesn’t speak to me, which only happens with one in ten resumes anyway, I’m not even going to call the candidate.”

Resume Red Flag: Lengthy Resumes

The takeaway here? You need to prioritize. Condense your experience to the most-important, most-relevant points so it’s easy to scan.

Lengthy resumes over a page long are no-gos for recruiters! 

Step #2: Customize, Customize, Customize!

Think about it. If you’re a hiring manager, are you more impressed with a generic resume or one that caters to your specific company culture and job requirements? Exactly. 

Yes, it requires more upfront work than the copy-paste approach. But adding small details here and there in accordance with the specific job will most certainly earn you points.

Does that mean you need to do a wholesale rewrite and redesign every time you apply for a job? No. But, at a minimum, look for important keywords and skills mentioned in the job posting and be sure to include those on your resume.

Pro-Tip: Dial In Your Writing Style and Tone

If you’re applying for your dream job, and you’re really looking to impress, you can take things up a notch. Here’s how: Take a look at the company’s website to get an idea of their preferred style and tone. Then, adjust the writing and aesthetics of your resume accordingly.

“You have to find a way to structure yourself such that when an employer is looking at your resume, they go, ‘This person was sent down from the heavens just for my particular position,’” says SharpestMinds co-founder Edouard Harris.

Step #3: Choose Your Template Wisely

The type of resume template you choose should align with the type of company you’re applying to. 

For example, if you’re applying to companies with a more traditional feel (e.g., the Dells, HPs, and IBMs of the world), try to aim for a more classic, subdued style of resume.

conservative-resume-templates

On the other hand, if you’re aiming for a company with more of a startup culture or creative vibe (e.g., Google, Meta, Pinterest, etc.), you can choose a template or create a resume with a little more flair.

creative-resume-templates-1

The template you choose can also be an organizational tool. A column-style resume can help you fit more information on the page, for example.

Pro-Tip: Use an Online Resume Template

Can you create your own resume from scratch? Sure. But it may be easier to start with creative resume templates from free sites such as CreddleVisualCV, CVMKR, or Enhancv. You could even use a Google Doc resume template.

Step #4: Curate Your Contact Info

Once you choose a resume template, take a second to double-check the contact information section. Your name, headline, and contact information should always be visible at the top of the page.

Why? You don’t want a recruiter or hiring manager to have to search through the whole resume to figure out how to get in touch with you, do you? Make it easy for them! 

Here are some key things to remember about the contact information on your data science resume:

  1. Simplify your address to just the city and state.
  2. List a good, working phone number and a professional-looking email address.
  3. Include your personalized LinkedIn URL.
  4. Add a GitHub link or personal profile link to your contact information, and make it clickable

Make sure your headline (typically found underneath your name) reflects the job you’re looking to get rather than the job you currently have. If your desire is to become a data scientist, your headline should say “Data Scientist” even if you’re currently working as a chef, for example.

resume-contact-details-data-science

Step #5: Include Data Science Projects and Publications

In any good data science resume, the main thing you want to highlight is what you have created. Include a separate section dedicated to your data science projects and publications. Place this information immediately following your name, headline, and contact information.

Hiring companies want to see what you can actually do with your listed skills. This might include data analysis projects, machine learning projects, and even published scientific articles or coding tutorials

Most data science employers will want to look at your project portfolio with an eye for how much regular work you’re doing and what kinds of projects you’re working on. 

“Most of the folks that we interview have their GitHub pages listed on their resume,” says CiBo Technologies talent acquisition manager Jamieson Vasquez. “I think that is important.”

Showcase Relevant Projects

When selecting which projects to highlight on your resume, keep one important factor in mind: relevance. Choose only those projects relevant to the job you’re applying for. Pramp CEO Refael “Rafi” Zikavashvili explains why: 

“Data scientists have one goal, and that is to solve business problems. It’s not about how technically difficult the challenge is, it’s not about how cool the solution is, or the tools that you’re using. It’s about whether you were able to solve business problems.

How many projects/publications should you list? As many as you can fit within the one-page parameter. 

Red Flag: Data Science Resumes with No Projects or Publications

Resumes without any projects or publications will definitely send up a red flag for hiring managers.This void will not go unnoticed. Instead, it will highlight your inexperience and make recruiters wonder why you even bothered to apply! 

Need help putting together projects for your resume and portfolio? We have a whole series of blog posts to guide you through building great data science projects. Plus, the next chapter in this guide discusses what projects you should showcase in a job application and how.

Highlight Your Skills

Be as specific as possible about the skills, tools, and technologies you used in each project. Specify the coding language, any libraries you used, etc. Talk about how you created the project, and in the case of group projects, point out your individual contribution. 

Pro-Tip: Redundancy Is Okay! 

Feel like you’re repeating the same skills in the projects section as you plan to list in your skills section? No worries. In fact, the more times you can add those key tools, technologies, and skills in your resume, the better.

Why? Recruiters and hiring managers often use simple keyword searches to scan resumes. So, you want your relevant skills highlighted in as many spots as possible!

Emphasize Communication Skills

Data science recruiters are looking for people who have the technical skills that they need, sure. But they also want people who are effective communicators and who understand the big picture. They want data scientists who can effectively tell stories with data.

One way you can demonstrate these traits is by highlighting collaborative projects. This proves you can work and communicate with a team. 

Another way is by framing your accomplishments in the context of business metrics. This shows you understand how your analyses apply to the bigger business problems you’re trying to solve.

Write your projects and work experience sections with these ideas in mind.

Make Your Projects Stand Out

There will likely be many applications for the job you want. How can you give your application that competitive edge it needs? Here are two more strategies to consider. 

Mention Unstructured Data

That is, any data you’ve worked with that required you to build spreadsheets/data tables yourself.

Examples of this could be working with videos, posts, blogs, customer reviews, and audio. Experience working with unstructured data is impressive. It shows you’re capable of doing unique work with messy data, not just crunching numbers in pristine datasets.

Identify Measurable Results

“If you want to take your resume from good to great, make sure you list measurable achievements,” says Zety.com recruiter Ewa Zakrzewska.

For example, if you created a machine learning model that would improve sales targeting by 15% as one of your projects, say that! 

“‘This is the thing I was trying to do, this is what I did, and these are the results.’ Laying projects out like that really creates a powerful resume,” says Michael Hupp, data science and analytics manager at G2 Crowd.

Here’s a sample of what this section of your resume might look like:

resume-projects-data-science

Step #6: Detail Your Relevant Work Experience

Next comes your work experience. Your most recent work experience should be listed on top, with the preceding job below that, and so on in chronological order.

How far should you go back? That depends. Five years is usually the cap, but if you have relevant work experience that goes back further than that, you may want to include it.

Resume Red Flag: Gaps in Work History

Keep in mind that gaps of longer than six months in your work experience section are a major red flag for recruiters and hiring managers. If you have such a gap, you most definitely want to explain it on your resume.

For example, if you took two years off to raise children between 2015 and 2017, you still want to add those dates on your resume. Simply state that you were a stay-at-home parent during that period.

Writing the Job Entries

When writing this section, each entry should include the following: 

  • your job title
  • the company
  • the period of time you held the position
  • your accomplishments in that role

If you have relevant work experience to the job you’re applying for, make sure your description consists of mostly accomplishments rather than duties. Employers want to see what you actually did, not just what you were supposed to do.

If your work experience is not relevant to the job you’re applying for, then you’ll still want to list it. But, you only need to include a company name, your job title, and the dates worked. You don’t need to take up space with all the details of an irrelevant job.

Here’s an example of what you might include for a relevant job:

resume-experience-data-science

Step #7: List Your Education

Many resume templates list education first. But if you’ve got work experience and/or relevant projects to showcase, you’ll want to show those off first and put education closer to the bottom.

List only post-secondary degrees (i.e., community college, college, and graduate degrees). If you went to college but did not receive a degree, it is best not to list that school. 

What if your degree is not relevant to the job you’re applying for? You should still list it. Some positions simply require a degree in any field, so you want to ensure you’re in the running for these positions.

Pro-Tip: Don’t Forget Your Data Science Certificates! 

Finally, list relevant “micro-degrees,” online training certifications, and other professional training here. 

Data science certificates like those offered through Dataquest are great to add here. That’s because they can show recruiters targeted skills. Plus, each Dataquest course includes an opportunity to create skills-based projects that you can also include on your resume! 

Resume Red Flags: Older Degrees and High School Diplomas

If the graduation date for your degree is 15+ years back, use your discretion about whether you want to include a date or not. Unfortunately, some companies see a graduation date starting with 19XX as a red flag.If you don’t have a degree, don’t sweat it. Just leave the Education section completely off of your resume. What you don’t want to do is list your high school information. This is another red flag for recruiters and hiring managers.

resume-education-data-science

Step #8: Add Skills and Extras

There are a couple more ways you can show off your skills in addition to listing your data science projects and publications:

  • Include the relevant skills you have learned in a “Skills” section.
  • Add an “Extras” section with relevant activities and training.

The Skills Section Is Not Optional!

Recruiters and hiring managers will most likely do a keyword search as a first step in viewing your resume. You want to make sure key terms like “Python” or “machine learning” are highlighted. 

Recruiters assume that the skills you list first are your strongest skills, and the skills you list last are your weakest. For that reason, list your strongest and most relevant skills first. Leave skills where you’re less comfortable or that are less likely to be relevant to the position for later in your list.

Resume Red Flag: Skills You Can’t Explain

You want to be careful not to go overboard here.

“I think a huge red flag is putting too many technologies on [a resume] and then not being able to back them up, especially in a phone call,” says Clay McLeod, manager of Bioinformatics Software Development at St. Jude Children’s Research Hospital.

Stephanie Leuck, a university recruiter at 84.51°, sees thousands of entry-level data science resumes a year. She echoed this sentiment. “Make sure [the skills you list on a resume] are skills that you can actually speak to. If you read a book once about R, but you can’t actually code in R and you’ve never coded in R, don’t list R as one of your skills. Only put skills on there that you can speak to.”

Pro-Tip: Don’t List Soft Skills

Should you include soft skills here? Probably not. Recruiters tend to scan this section looking for the specific technical skills they need. Stating that you’re skilled in “communication” or a “team player” isn’t going to help your resume stand out.

It’s better to show that you have these skills in the project and work experience sections. Here, you can highlight the ways your “soft” and “hard” skills have functioned together to produce meaningful results.

When to Add an Extras Section

If you have the room, consider including an “Extras” section. This section can be labeled Awards, Certifications, or Training, for instance.

In the data science realm, you have a few options for this section. For example, you might want to list any good Kaggle competition results you’ve had or relevant meetups/events you’ve participated in. Anything else that demonstrates you’re actively involved in learning and doing data science is also fair game.

Data science and machine learning hackathons like Machinehack and Hackerearth are a huge plus on your resume. It shows you have a healthy competitive spirit. Plus, it lets recruiters know you can enhance your skills and knowledge while creating actual content and projects.

Here’s a sample of what the skills and extras sections might look like:

skills-experiences-data-science-resume-1

Polish With Finishing Touches

Once you’re finished adding all of the relevant content to your resume, the last major thing to do is a spelling and grammar check. A huge red flag for recruiters and hiring managers is having grammatical or spelling errors on your resume.

Resume Red Flag: Errors and Typos

Remember, recruiters often get hundreds or thousands of applications for entry-level jobs. They’re often looking for any excuse possible to weed out candidates!

Although it might seem minor, a simple typo suggests a lack of attention to detail. Believe it or not, that’s enough for some recruiters to toss out your resume regardless of the skills and experience you have.

So make sure the writing is free of errors and everything is phrased simply and clearly. Having an editor friend give your resume a check is always a good idea, but apps like Hemingway and Grammarly can also help you clean up and simplify your writing.

Pro-Tip: Use a Human Editor

Be careful not to put too much trust in automated grammar-check software. Even the best apps make mistakes.

A finished data science resume might look something like this:

data-science-resume-template

Of course, a resume doesn’t mean much if you can’t prove you’ve got the skills it lists. In the next chapter, we’re going to take a deeper look at what kinds of projects you should be doing, and how you should be highlighting them in your portfolio.

This article is part of our in-depth Data Science Career Guide.

Become a Data Analyst!

Learn the skills you need to work as a data analyst today. Sign up for a free account and get access to free interactive Python, R, and SQL course content.

Sign up now!

(No credit card required!)

Charlie Custer

About the author

Charlie Custer

Charlie is a student of data science, and also a content marketer at Dataquest. In his free time, he's learning to mountain bike and making videos about it.

Learn data skills for free

Headshot Headshot

Join 1M+ learners

Try free courses