Employer demand for data science professionals has grown exponentially over the past several years. As applicants attempt to enter this increasingly competitive field, many struggle with the same challenge: nailing the data scientist resume.
These seven steps will help you build a comprehensive data science resume and CV from scratch that will stand out to recruiters and hiring managers.
7 Tips for Your Data Scientist Resume
1. Do Your Research
Employers care less about you wanting a career in data science than they do about you wanting a career with them. Before you start hacking together a data science resume, make sure you know who you’re sending the resume to.
Realistically, your resume won’t be wildly different for each application you file, but it should be somewhat different.
“A tailored resume separates applicants who just want any job from those who want this job,” said Jon Brodsky, country manager for Finder.com.
The importance of this process is usually encapsulated in one word: fit. Are you a good fit for the company? Does your data science resume reflect the fact that you’re a good fit?
“At most companies, and certainly here at Finder, the hiring manager will also be scanning your resume to gauge whether or not you are a good cultural fit,” Brodsky said.
In addition to researching for fit, it’s important to pick a template that’s right for you, as well as one that demonstrates your potential fit to the recruiter.
How do you research for your data science career resume?
Read and re-read the job description: The job description is the most important piece of information to keep in mind. Your resume should demonstrate that you fill the job description: in experience, in skills, in location, etc.
Read the “About” page: So you found a position at a company and you know nothing about it? The best place to start is the “About” page, or the page that gives an overview of the company, its mission, its values, etc.
Check out the company blog: If the company has a blog, read through it. This will give you a lot of detail about what it’s trying to do, who its target market is, the company voice, and much more.
Browse product pages and other site pages: Find out what the company is selling or doing. Make sure you’ve gotten a good birds-eye-view understanding of the company and its site, keeping in mind how you can help increase revenue from a data scientist’s point of view.
Scour the internet: Once you’ve finished browsing the company site, expand your search. Good external resources for learning what you might want to know include glassdoor.com, LinkedIn, and various media outlets that might have published articles and press releases related to the company.
Tip: This doesn’t have to take more than 30 minutes.
2. Pick a Resume Template and Don’t Sweat the Design (Too Much)
We live in a complex world of media wherein the traditional black-and-white resume isn’t always the first choice for many applicants. Simply search “cool resume designs,” and you’ll find some objectively beautiful resume templates and samples.
Tip: Remember that just because a so-called “design resume” looks good doesn’t mean it is good or even effective.
You can select a pre-existing template, design your own, hire a professional graphic designer, and much more. Nonetheless, some experts would say that you’re overthinking it.
“The format of the resume itself doesn’t matter, but it should be brief, one page maximum,” Brodsky said. “Hiring managers are busy and often inundated with resumes, so will only skim-read the first page anyway.”
What that means: Pick a resume design that employers can skim, not one they have to read. You don’t need fancy colors or logos on your resume. You don’t need a multimedia resume. You don’t need the best icons or visuals.
However, if you use those, make sure they’re a reflection of you and not of a graphic designer you hired.
In other words, feel free to get creative to some degree, as long as it’s not distracting from your qualifications or coming off as too gimmicky. Personally, I like to find a happy medium between silent design and professionalism.
Microsoft Word offers plenty of resume templates, but before you select one, a good exercise is to create “dummy sheets” of your own in order to visualize and organize your resume layout. Just get a pen and a piece of paper and draw it out. Here are a few dummy sheets I created when designing my own resume.
3. Organize Your Data Science Resume Template
In 2012, TheLadders released a study that shook the internet. It said that recruiters take only six seconds to review a resume, on average. Now everybody writing about resumes cites that figure. If that’s true, then, as the report notes, “an organized layout is crucial.”
To get started, decide on the information you’ll include, using headings and subheadings. A resume could be organized this way, for example:
Education & Certificates
Skills & Knowledge
The order of this matters. Resumes are typically read from top-left to bottom-right. In other words, what’s most important should be at the top-left. The least important items should be at the bottom-right. What’s most important for entry-level data scientist recruiters, aside from who you are, is probably education, so our example has that at the top, just below name and contact information.
Space is also an important consideration. Those items that take up the most space are the meat of your resume, and that should always focus on relevant experiences.
Tip: Experience should always rank high in the organizational hierarchy of any resume.
4. Write the Resume
Let’s get to writing that resume for you soon-to-be data scientists.
Disclaimer: This resume is hypothetical.
We’ll get the name out of the way first.
777 Springboard St. #144, Los Angeles, CA 90001
Congratulate yourself. You’ve written the first part of your data science resume. Just make sure it’s accurate. This is the spot where typographical errors could ruin any chance you have at a phone screen, much less an interview. It has to be right.
Education & Certificates
Because an entry-level candidate might be a little thin on direct experience, education becomes more important. Manjunath Thandu, founder and CEO of Zavoke, said that he’d like candidates to have at least a BS in data science or a related field, but he prefers a Ph.D.
Brodsky believes you should list degrees if you have them, but adds that it’s important to list certificates, extracurriculars, or relevant coursework as well, not necessarily just degrees. For this exercise, let’s assume the best of both worlds: you have a degree and a certificate.
Education & Certificates
Bachelor of Science, Data Science
Data Scientist Certificate
An education like that should be sufficient for many entry-level positions. Now let’s move on to the big one: experience.
This is the most important part of just about any resume, including the data science resume. For an entry-level data scientist’s resume, you might have some difficulty listing the experience you have while keeping it relevant.
Nonetheless, Brodsky encourages candidates to list what they have done, saying, “Don’t be afraid to list interests or passion projects outside of work or use quirky language or style if that best reflects you.”
Let’s say the most relevant experience we have included is an internship, which would be a good position to be in for a recent college graduate looking to land an entry-level position. Otherwise, our experience lies mostly in personal projects and coursework.
Data Science Intern
141 Random Circle Suite #999, Los Angeles, CA 90001
May 2017 – August 2018
Report actionable, statistical, and analytical insights to executives for effective strategic positioning in marketplace
Shadow data scientists and assist in developing algorithms for predictive modeling
Analyze and process sophisticated data sets using SAS, MySQL, and Excel
Data Science Capstone Project: Specialized Analysis of Streaming Platform Patterns
Springboard Data Science Course
Analyzed data from 233 streamers in 30 countries across five different platforms over 30 days, totalling nearly 300,000 hours of streaming
Confirmed hypothesis that data supports an upward trend of night-time streaming
Tip: Notice the use of strong verbs in a parallel structure. Strong verbs are a staple of strong writing, whether it be for resumes or otherwise.
Skills & Knowledge
Skills are an important section in a data science resume, as there are many complex tools and programmatic languages that employers expect of their candidates. Some people simply list skills, others list skills with an evaluation of their familiarity, while others list skills and a description of where and how they used them. Because we’re looking for brevity, and because the goal of the data scientist resume is to land an interview, not the job (yet), we’ll simply list skills here.
Thandu lists skills such as programming languages (R and Python) and database and warehousing knowledge. Additional skills might include knowledge of SAS (and other analytical tools) or data mining and processing.
Tip: Listing skills provides a summary of your data science qualifications, but does not provide evidence that you can use them. Make sure your skills tie into your experience somehow.
Skills & Knowledge
Languages: R, MySQL Python, PHP, C
Technical Skills: Data mining and processing, statistical analysis
Soft Skills: Business acumen, oral and written communication, strategic planning
Keep this section short, as it’s more of a “tell” than a “show” section, which is why it appears toward the bottom of the resume. It’s most valuable for employers who are skimming.
5. Tweak It for Fit
Let’s say you want to work for Google. A lot of people do. Your research is key in identifying how you might fit into an organization. Fortunately, as visible as Google is, the company has already laid how it hires candidates, in episode 12 of its partners podcast. The attributes it looks for in candidates include:
- General cognitive ability
- Role-related knowledge
Let’s focus on two of these attributes: general cognitive ability and “Googliness,” as these, unlike the others, are not as easily shown in a traditional resume.
General cognitive ability is a measure of your creativity, problem-solving, innovation, and much more. A representative from Google said in the podcast that this attribute is more important to them than role-related knowledge—good news for entry-level data science candidates who may not have direct experience in the role.
What does this mean and how can you demonstrate “general cognitive ability” on your resume, apart from listing it as a soft skill? (Don’t do that.)
In many ways, you can’t. Most of the time, that’s measured through tests and interviews. However, it doesn’t hurt to write out descriptions of problems you’ve solved in one of your job roles. Let’s add a bullet to our resume that describes an unexpected problem solved in our role as a data science intern.
- Wrote Python scripts to automate everyday tasks such as organizing desktop files, automatically redefining strings in MySQL database to promote consistency, and creating file and folder structures for new projects
To some, this might seem unimpressive. To others, this reads: creativity, initiative (assuming no one asked you to do these things), and ingenuity—in other words: general cognitive ability.
“Googliness” is Google’s way of saying “culture fit.” In my own resume, knowing that Google values the renaissance person, I might include skills that don’t necessarily relate to my role. I like to play guitar, and I like to write. Let’s add an awards section in the resume that reflects those skills.
1st Place | Walnut Festival International Fingerstyle Guitar Championship
2nd Place | Writers of the Future Contest ($750 prize)
Targeting for fit isn’t an exact science, as you can see. Just do your research and make room for details you might not have included if it were another employer. (Wouldn’t it be a match made in heaven if you happened to win the Doodle for Google prize as a kid?)
Keep it relevant. Don’t waste the employer’s time by submitting a resume that doesn’t persuade them that you have the necessary qualifications. Hit the keywords in the job description. Mention responsibilities you had that directly correlate with those of existing employees in the position. List skills necessary for the position. Show that you have the attitude and tone that the company is looking for. The goal is to weave the research you’ve done into the perfect data science resume for the particular position for which you’re applying.
Keep it professional: That word “professional” packs quite a wallop here, and has many different meanings.
- Clean, judicious design that omits frills and bullspit.
- Correct grammar, spelling, and typography.
- Relevance. Don’t waste their time by blowing smoke.
This ties quite nicely into relevance. If you’ve done your research, it should be easy enough to discern what’s most and least important for your resume. Is your high school basketball career the defining feature of your data science resume? No, but if you crunched the stats for every college basketball game and reported those for your university media outlets, you might have some more relevant material to include.
Keep it readable: If I had to pick one thing to do in terms of the format, I’d implore you to use some form of bullet points in your resume. Simply put, bulleted resumes are far more legible than resumes with blocks of text.
Keep it to one page: Sure, not all resumes include a single-page design, but we want an excellent data science resume. Everyone knows the number of pages in a resume design is hotly debated. Yes, some hiring managers will be fine with more than a page. At the same time, no one will flip tables if your resume is a single page.
6. Pull It All Together
Now we’ll try to fit our content into a layout. I’m partial to a two-column design, as it wastes less space than single-column, center-aligned layouts (a more conservative and popular option).Notice there’s quite a bit of white space still. That may not be the case with your own resume, as you might have more to list. Nonetheless, don’t worry too much about white space.
Tip: It might take some sorcery to fit everything in the layout and keep it visually pleasing. Just work with it and adjust phrasing and formatting as needed.
Tip: If you need some data scientist resume samples do a quick Google image search and use the results as inspiration.
7. Send It to an Actual Person, If You Can Help It
It shouldn’t come as a surprise to a data scientist that resumes don’t always reach the eyes of a human being. Much of the data you “unicorns” mine is collected and processed through machine learning. So the advice that you should try to get your resume into the hands of a recruiter might seem counterintuitive. Don’t rely on machine learning to decide your fate (à la an applicant tracking system). Just like the data scientist is needed to parse, interpret, and communicate data from all sources, a recruiter is needed to do the same for resumes.
That said, it’s not a bad idea to develop a resume specifically tailored toward applicant tracking systems. ATS parses resumes, devoid of formatting and design concerns, based on text analysis. Dustin Polk, a resume designer and career counselor at Oracle Resumes, recommends having a .txt resume on hand for just that purpose.
“Use keywords from job ads for positions you may be interested in,” Polk said. These will help you get through any ATS your future employers may be using and give you ideas about what to say about yourself.”
The problem with ATS is that it doesn’t always accurately capture your fit for a position. It doesn’t understand context or connotation well, so do your best to get your resume in front of real eyeballs.
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Remember that the resume is only one step in the application process, and its sole objective is to land interviews. If your resume isn’t up to scratch, it shouldn’t be because you organized it wrong or because you made it two pages. It should be because you simply don’t have the relevant experiences, education, or skills.
Ready for more? To further understand what you need to know start your data science career, check out Springboard’s guide on How to Get Your First Job in Data Science today.