How to Write a Great Data Science Resume
Writing a resume for data science job applications is rarely a fun task, but it is a necessary evil. The majority of companies require a resume in order to apply to any of their open jobs, and a resume is often the first layer of the process in getting past the “Gatekeeper” — the recruiter or hiring manager.
A resume ( résumé, CV), by definition, is a brief written account of your personal, educational, and professional qualifications and experience. Writing a brief summary of your own experiences sounds like an easy task, but many struggle with it. Here are some tips about how to write a clear and concise resume that will catch the eye of a recruiter/hiring manager.
(This article is a part of our in-depth Data Science Career Guide. To read the other articles, please refer to the table of contents or the links that follow this post.)
Keep Data Science Resumes Brief
The first thing you should strive for in writing a resume is to keep it short. A good resume should only be one page long, unless you have 15+ 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. Recruiters/hiring managers receive a LOT of resumes every day, and they usually have about 30 seconds to look over someone’s 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.”
So, although you might have dozens of data science projects you'd like to highlight, you need to prioritize. You want to boil your experience down to the most important, relevant points so it is easy to scan.
Customize Each Resume to the Job Description and Company
While you certainly can create a single data scientist resume and send that to every data job you apply for, it would also be a good idea to try and add customized tweaks to your resume for each application you submit. Although it requires more work up front, adding small details here and there in accordance to the job description would certainly impress the hiring manager/recruiter.
This doesn’t necessarily mean you need to do a wholesale rewrite and redesign every time you apply for a job! But, at a minimum, if you notice important keywords and skills mentioned in the job posting, you be sure the resume you’re sending highlights your skills in those areas, and makes use of those keywords. You may also want to take a look at the company’s website to try to get an idea of their preferred style and tone, and 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.
(Obvious, but still worth pointing out: don’t list any skills or experience that you don’t actually have. It’s fine to re-frame your real skills and experiences to fit the context of a job posting, but it’s not OK to exaggerate or make things up.)
Choose a Template
While every resume will always include information like past work experience, skills, contact information, etc., you should have a resume that is unique to you. That starts with the visual look of the resume, and there are many different ways to accomplish a unique design. You can create your own resume from scratch, but it may be easier to start with creative resume templates from free sites such as Creddle, Canva, VisualCV, CVMKR, SlashCV, or even a Google Doc resume template.
Keep in mind that the type of resume template you choose is also important. If you’re applying to companies with a more traditional feel (the Dells, HPs, and IBMs of the world), try to aim for a more classic, subdued style of resume.
If you’re aiming for a company with more of a startup vibe (Google, Facebook, Pinterest, etc.), you can choose a template or create a resume with a little bit more flair, perhaps with some graphics and unique coloring.
You can also choose between a column-style resume (usually better for people struggling to fit everything on one page) or a block-style resume where everything is stacked in one column.
Either way, keep it simple. Again, a hiring manager may only take 30 seconds to scan this document and make a decision, so when in doubt, keep things short and sweet. Don’t be afraid of white space in your resume design.
Just remember that although you start with a template, you should take the time to make it your own.
Once you choose a resume template or decide to create one from scratch, take a second to double-check the contact information section. Your name, headline, and contact information should always live at the top of the page.
Some templates will have the contact info located toward the bottom of the page. You will want to rearrange the order manually if that’s the case. If a recruiter or hiring manager decides to contact you based on your resume, you don’t want them to have to search through the whole resume before they find that information!
Key things to remember about your contact information and what to put there in the context of a data science resume specifically:
- You do not have to put in your whole physical address; all you need is the city and state you live in. It may be best to leave your location off completely if you’re applying for jobs in other cities (as long as you’re willing to relocate).
- Always make sure you have a good, working phone number and a professional-looking email address listed. A good email would be some combination of your first and last name, i.e. [email protected] or [email protected]. You don’t want to use a personal-looking email address like [email protected] on a resume.
- You should include your LinkedIn profile link, but you don’t want to just copy and paste the whole profile URL, as it will look clunky. You can create a shorter, more personalized profile URL on LinkedIn (directions here). This URL should be some version or iteration of your name, i.e. linkedin.com/in/firstnamelastname/. Or you can simply use a URL shortening service such as bit.ly.
- You should add a GitHub link or personal profile link to your contact information, and make it clickable. You’re applying for data science jobs, so most employers are going to want to take a look at your portfolio to see what kinds of projects you’re working on (and to see that you’re working regularly; we’ll discuss this in more detail in our chapter on project portfolios).
“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.”
These links should be clickable in all PDF versions of your resume so recruiters can navigate directly to your profiles rather than having to copy and paste. And, needless to say, when a recruiter clicks through to your GitHub, they should find an active account with data science projects!
- 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 you’re trying to become a data scientist, your headline should say “Data Scientist” even if you’re currently working as a chef.
Data Science Projects and Publications
Immediately following your name, headline, and contact information should be your Projects/Publications section. In any resume, especially in the technology industry, the main thing you want to highlight is what you have created.
In the context of a data science resume (or data analyst resume), this might include data analysis projects, machine learning projects, and even published scientific articles or coding tutorials. Hiring companies want to see what you can actually do with your listed skills. This is the section where you can show off.
Choose which projects you showcase with one important factor in mind, says Pramp CEO Refael “Rafi” Zikavashvili: “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.”
Thus, while you can definitely include personal projects on a resume, you should pick ones with some relevance or connection to the job you’re applying for. Projects should demonstrate your technical skills, but ideally, they will also demonstrate how your technical skills are applicable to solving real business problems.
You definitely want at least one project or publication on your resume, but if you have space for more, add as many as you can neatly fit. If you 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, and the next chapter in this guide discusses what projects you should showcase in a job application and how you should showcase them.
When you describe each project, be as specific as possible about the skills, tools, and technologies you used, how you created the project, and what your individual contribution was if you’re highlighting group projects. Specify the coding language, any libraries you used, etc.
Don’t worry if it feels as if you’re repeating the same skills you plan to list in your skills section. In fact, the more times you can add those key tools, technologies, and skills in your resume, the better. Recruiters and hiring managers often use simple keyword searches to scan resumes, and you want your relevant skills highlighted in as many spots as possible when they search your resume.
At the same time, remember that a data scientist’s job isn’t just to crunch numbers, it’s to analyze data and then communicate those findings in a way that solves business problems. Data science recruiters are looking for people who not only have the technical skills that they need, but also people who are effective communicators and who understand the big picture. They want data scientists who can effectively story-tell with data.
One way you can demonstrate these traits is by highlighting collaborative projects (which proves you can work and communicate with a team) and framing your accomplishments in the context of business metrics (which proves 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.
Another good way you can stand out from the herd in this section is with any mention of working with unstructured data — 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 because it shows you’re capable of doing unique work with messy data, not just crunching numbers in pristine datasets.
Wherever possible, you also want to make sure you’re indicating any measurable results your projects have generated. For example, if you created a machine learning model that would improve sales targeting by 15% as one of your projects, say that! “If you want to take your resume from good to great, make sure you list measurable achievements,” says Zety.com recruiter Ewa Zakrzewska.
“‘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:
Next comes your work experience. You can label this section “Experience” or “Professional 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 back you go in terms of experience is dependent on a few things. Typically you wouldn’t want to go back further than five years. However, if you have relevant work experience that goes back further than that, you may want to include that experience as well.
Keep in mind that while you don’t have to list all of your experience, you want to be sure that whatever you list looks seamless. 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 and state that you were a stay-at-home parent during that period.
When writing this section, each entry should include your job title, the company, the period of time you held the position, and your accomplishments in that role. Keep the formatting uniform across your resume, but particularly in this section: if you use filled-in bullets for your description of one job, make sure you use the same exact bullet format for all the other job descriptions, too. The same thing goes for how you list dates on your resume; if you’re spelling out the whole month for each work experience date, then make sure you do this in each place on the resume where a date is included.
If you have relevant work experience to the job you’re applying for (i.e. prior work that’s relevant to data science, analytics, etc.), 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. And remember, framing your data science accomplishments in the context of business metrics is a good way to demonstrate that you understand the big picture and know how to translate your analysis results into real business outcomes.
If your work experience is not relevant to the job you’re applying for, then 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:
Although it is great to have a degree, you probably don’t want to highlight that first on your resume unless you’re a graduating student looking for their first job in a relevant field. 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.
The only things you should list in the Education section are 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. 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.
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 add your high school information under your Education section as this is another red flag for recruiters and hiring managers.
Finally, don’t list “micro-degrees,” online training certificates, or other professional training here. Many data scientists have this sort of training, and it's fine to include these certificates on a data science resume, but they shouldn't be listed under Education. We’ll include them elsewhere on your resume.
Skills, Certificates, and Extras
If you’re trying to find your first job in data science, it can be difficult to demonstrate you have the relevant skills and experience on your resume. But there are a couple of different ways you can show off your skills in addition to listing your data science projects and publications:
- Including the relevant skills you have learned in a Skills section
- Adding an “Extras” section with relevant activities and training.
The skills section isn’t optional; for technical positions, this is a necessity. Recruiters and hiring managers will most likely do a keyword search as a first step in viewing your resume, so you want to make sure key terms like “Python” or “Machine learning” are highlighted. Only list technical skills or tools here; you do not need to list soft skills like leadership or communication.
Some resume templates may ask you to “rank” yourself for each skill, but it’s better if you don’t list a ranking on your resume. You don’t ever want to overpromise or sell yourself short. The way a recruiter or hiring manager looks at your skills is by assuming 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, and leave skills where you’re less comfortable or that are less likely to be relevant to the position for later in your list.
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. “If you put something on your resume, I should be able to ask you at least the basics and make sure that you understand those things. Don’t put things on your resume that you’re not willing to be asked in an interview about.”
Stephanie Leuck, a university recruiter at 84.51°, sees thousands of entry-level data science resumes a year and 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.”
If you’ve done all of the above and still have space to fill in your resume, another way to show that you’re continuing to learn or grow in your desired field is by having an “Extras” section. This section can be labeled Awards, Certifications, or Training, or anything else that seems appropriate and professional. In the data science realm, you might want to list any good Kaggle competition results you’ve had, any online certificates you’ve earned (this is where you list your data science certificates and/or progress), meetup/events you’ve participated in that were relevant and meaningful, and anything else that demonstrates you’re actively involved in learning and doing data science.
Data science and machine learning hackathons are a huge plus on your resume. It shows you have a healthy competitive spirit and you can enhance your skills and knowledge in your field while creating actual content and projects; these can also be in the Extras section. (Check out sites like Machinehack and Hackerearth if you’re interested in participating in hackathons but haven’t joined any yet.)
Here’s a sample of what the skills and extras sections might look like:
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.
These can be hard to catch yourself, so have a trusted friend (or a few) do a peer review of your resume and give you feedback. They may catch small errors that you missed!
Remember that because recruiters often get hundreds or thousands of applications for entry-level jobs, they’re often looking for any excuse to weed out candidates. Although it might seem minor, a simple typo suggests a lack of attention to detail, and that would be enough for some recruiters to toss out your resume regardless of the skills and experience you have.
A finished data science resume might look something like this:
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.
- Introduction and Table of Contents
- Before You Apply: Considering Your Options
- How and Where to Find Data Science Jobs
- How to Write a Data Science Resume — You are here.
- How to Create a Data Science Project Portfolio
- How to Fill in Application Forms, When to Apply, and Other Considerations
- Preparing for Job Interviews in Data Science
- Assessing and Negotiating Job Offers
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