7 Steps to a Job-winning Data Science Resume

A resume plays a key role in bagging that dream data science job. We break down the nuances of a job-winning data science resume so that you can go ahead and transform your own resume.

By Aditya Sharma, Co-Founder at Hiration

According to LinkedIn's 2017 U.S. Emerging Jobs Report, data scientist jobs have been ranked second in its Top 20 List of Emerging Jobs, with a 6.5x jump in growth (2012-2017).

Given the unprecedented demand for data scientists across all industries worldwide, you might end up thinking that bagging a data science job is a walk in the park.

But this is where you’re highly mistaken.

Getting certified in data science or participating in Kaggle competitions is not enough to land you a job in this industry. You might have cultivated the skills of the trade but there’s no point being exceptionally skilled if you fail at demonstrating the same qualities on paper.

Which is why your data science resume matters.

In this article, we’ll discuss some actionable steps you can take to make a killer data science resume and land that dream job.

Without further ado, let’s begin.


Your name comes first. Not ‘CV’ or ‘Resume’

Of what good is your resume if it gets misplaced in a huge pile just because you forgot to add your name at the top?

As surprising as it sounds, we do encounter such cases frequently, which is why we thought to put it out there before we discuss the specifics of a data science resume.

As a general practice, make sure that your name features on the top. No need to write ‘CV’ or ‘Resume’ on the top since the nature of the document is evident.


Go beyond your basic contact details

You know the basic contact details that go in your resume, right? Just to recap, they are your:

  • Mobile number: Just mentioning one will suffice.
  • Email id: Go for a professional email id not affiliated to your present/previous employer.
  • Location: You can mention your present location. In case you are relocating, skip those details from your resume and mention that in the cover letter instead.

But that’s not it.

As a data scientist, you are driving a revolution. In addition to these basic details, feel free to add your portfolio or other public details spanning LinkedIn, GitHub, Kaggle, etc.

Only do that if it’ll add value to your profile. If you’re linking to an empty LinkedIn profile or a GitHub account that was last updated months ago, there’s no point. If there are significant achievements on GitHub, Kaggle or any other public platform, feel free to mention the same in a separate section in your resume.

However, doing that only makes sense if your work-ex section lacks concrete content around data science. If your work-ex itself is a testament to your skills, you don’t need to put additional focus on these achievements to validate the same.

But if you’re transitioning to data science, correctly showcasing these achievements will play a huge role in getting that coveted shortlist.


Your profile title might just make or break your chances

First off, understand that not many people have a profile title on their resume. So, adding the same will go a long way in overshadowing your competition.

But before we continue, what’s a profile (or job) title anyway?

It is nothing but an identification marker right below your name that communicates your professional identity. The recruiters shouldn’t have to scan your work-ex to gauge whether you’re relevant to them. You’re adding a job title to make their job easier.

Here’s a quick tip on how you can formulate your profile title according to the career stage you are in:


You have a certification in Data Science but not any relevant work-ex

Let’s say you received a data science certification as part of any online program but you don’t have any work experience yet.

You can simply write “Certified Data Science Professional” as your profile title on your resume.

Doing this communicates in one go that you have the relevant certification even if you don’t have the professional experience yet.


You have the relevant work experience

Let’s say you have worked as a data science engineer or a data scientist. In such cases, simply mention your current designation as the job title below your name.

Let’s say you are working as a Data Analyst and your target profile is that of a Data Engineer. Don’t try to fool the recruiter and write “Data Engineer” as your job title. Sticking to the truth is the only sustainable way to get what you want.


You are transitioning to data science from a related (or unrelated) domain

In case you are transitioning to data science, mention your current/last-held designation. In case you’re transitioning, you need to convince the recruiter that you’re serious about data science, even though you don’t have any relevant work-ex.

If you have any certifications to help your cause, we already covered that in the first point. If you don’t have that, you can mention your projects or competitions in the relevant section. In that case, the job title can turn into something along the lines of “<current designation> & Data Science Enthusiast”.


Less data science and more “IMPACT” in your Summary

A summary would be a brief 3-4-line statement describing the impact you can deliver in the next organization. Don’t go heavy with your data science skills here - that’s what the rest of the resume is for. In summary, simply mention the value you can deliver using your specific skills.

Don’t go into the technicalities of techniques, algorithms, and libraries you’re familiar with. Mention the business impact you can deliver: sound decision making, improved processes, leadership support, etc.


Divide your skills into “Key Skills” and “Technical Skills”

As a data scientist, separate your core skills from your technical skills. Doing this helps you outline and communicate your skills more effectively.

The snapshot below showcases what this section should look like in an ideal data science resume:


Key Skills and Technical Skills section in a sample Data Science Resume


Grouping your technical skills under relevant subheadings makes it easier for the recruiter to quickly read what’s important and skip the rest.


Quantify your achievements in the Professional Experience Section

Recruiters love nothing more than a practical demonstration of the results that you can bring to the organization. It doesn’t matter to a company if you have the right skills - if you are not able to showcase results using those skills, you might as well not have them.

What’s the takeaway from this? Quantify your achievements wherever possible.


  • Use one-liner points to showcase your major achievements using action-oriented accomplishment statements.
  • Group similar points under unique subheadings or buckets. So instead of writing 10+ points in your work-ex, group them into buckets of 4-5 points each.
  • Begin each point with an action verb.
  • Quantify your achievements using performance figures. If you don’t have the exact numbers, even a ballpark figure will do.

Similarly, if you’re detailing your projects/competitions, provide approximate figures around your contribution, number of participants, etc. Here’s a snapshot of what a professionally-composed work experience section should look like in your data science resume:


Professional Experience section in a sample Data Science Resume



Illustrate your Certifications, Conferences, and Publications

The importance of certifications is a particularly crucial element in a data science resume as it communicates your updated skill-set in an ever-changing environment.

Alongside this, listing the conferences you have attended and academic papers you have published in your resume will also help make a favorable impression.


Key Takeaways

To sum up:

  • Write your full name in the resume header
  • Include your GitHub/LinkedIn/Kaggle details in addition to your basic contact details.
  • Correctly articulate your latest job designation as your profile title if you’re an experienced professional. If you don’t have relevant work experience yet, simply label your profile title as “Certified Data Science Professional”
  • Your resume summary should focus on the impact you can deliver and not be jargon-heavy.
  • Split your skills into Key Skills and Technical Skills, with the latter containing relevant subheadings like Languages, Libraries, Tools, etc.
  • List out your professional experience section using action verbs and performance figures.
  • Make sure that your resume outlines your certifications, publications, and conferences you have attended in the relevant sections.

That’s about it! Got any more queries around your data science resume? Drop a comment below!

PS: If you wish to deep-dive into the finer details of what we discussed here or want to check out a complete sample resume for data science, check out this exhaustive guide on data science resume.

Bio: Aditya Sharma is on a quest to help professionals across the world land their dream jobs. He lives and breathes Hiration — an AI-powered resume builder and platform to help job-seekers find their way in the treacherous job market — where he’s a Co-Founder and the unofficial CPO (Chief Problem-solving Officer).