How to Write a Great Data Science Resume

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 (or résumé, or 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.

Keep it 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. You want to boil your experience down to the most important points and make it easily scannable.

Customize it for the specific data science job

While you certainly can create a single resume and send that to every job you apply for, it would also be a good idea to try and add customized tweaks to your resume for each application you do. 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, you should look at the job posting, and if you notice important keywords and skills mentioned there that you have knowledge in, you should be adding them to your resume. 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.

(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 okay 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 look. 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 are 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.

How to Write a Great Data Science Resume

If you are 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.

How to Write a Great Data Science Resume

You can also choose between a column-style resume (usually better for people who are struggling to fit everything on one page) or a block-style resume where everything is stacked in one column. Either way, though, keep it simple. Again, a hiring manager may only be taking 30 seconds to scan this document and make a decision, so when in doubt, keep things short and sweet. Don’t be afraid of having some white space in your resume design.

Contact information

Once you have either chosen a resume template or decided 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 towards the bottom of the page, so 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 for that information.

Key things to remember about your contact information and what you choose to put there:

  1. You do not have to put in your whole physical address, all you need is the city and state that 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).

  2. 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. or You don’t want to use a personal-looking email addresses like on a resume.

  3. 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. Or you can simply use a URL shortening service like

  4. You probably also want to add a Github link or personal profile link to your contact information as well. 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 things you’re working on (and that you’re working regularly).

  5. Make sure your headline (typically found underneath your name) reflects the job you are looking to get rather than the job you have currently. If you’re trying to become a Data Scientist, your headline should say Data Scientist even if you’re currently working as a chef.

How to Write a Great Data Science Resume

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. For data scientists, this could include data analysis projects, machine learning projects, and even published scientific articles or coding tutorials.

However, keep in mind that hiring companies want to see what you can actually do with the skills that you have listed. This is the section where you can show that off. You can definitely include personal projects, but you should pick ones with some relevance or connection to the job you’re applying for.

You want at least one project or publication on your resume, but if you have the 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.

When you describe each project, be as specific as possible about the skills, tools, and technologies you used, how you went about creating 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 are 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, though, 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 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 in and communicate with a team) and by 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 section and your work experience section 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, i.e., any data you’ve worked with that required you to build spreadsheets/data tables yourself because it didn’t come to you in table format. Examples of this could be working with videos, posts, blogs, customer reviews, and audio, among others. Experience working with unstructured data is impressive to hiring managers/recruiters, as it shows you’re capable of doing unique work with messy data, not just crunching numbers in pristine datasets.

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

How to Write a Great Data Science Resume

Work experience

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 5 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 do want to be sure that whatever you do 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 of work to raise children between 2015 – 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 what you accomplished 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 are 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 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 are 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:

How to Write a Great Data Science Resume


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 probably want show those off first and put education closer to the bottom.

The only things you should be listing in the Education section are post-secondary degrees (i.e. college, community college, and graduate degrees). If you went to college but did not receive a degree, it is best not to list that school. But if your degree is not relevant to the job you are 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. We’ll include that information elsewhere on your resume.

How to Write a Great Data Science Resume

Skills, Certificates, and Extras

If you’re trying to find your first job in data science, it can be difficult to demonstrate you’ve got the relevant skills and experience on your resume. But there are a couple of different ways you can show off relevant 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 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, communication, etc.

Some resume templates may ask you to “rank” yourself for each skill, but it is better if you don’t list a ranking on your resume. You don’t ever want to over promise or sell yourself short. The way a recruiter or hiring manager will look at your skills is by assuming that the skills you list first are your stronger 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.

If you’ve done all of the above and still have space to fill in your resume, another way to show that you are continuing to learn or grow in your desired field is by having an “Extras” section. This section can be labeled Awards, or 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 as it shows that you have a healthy competitive spirit and you can enhance your skills and knowledge in your field while creating actual content and projects, and these could also be included 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:

How to Write a Great Data Science Resume

Finishing touches

Once you are 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 for you and give you feedback. They may catch small errors that you may have missed! A finished resume should look something like this:

How to Write a Great Data Science Resume

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