8 Steps To Make Your Data Scientist Resume Stand Out

There are 2.3 million data scientist jobs in the US and that number will only grow in the future. But this also means that there will be a lot of competition for these roles. The market already is quite competitive, so having a good resume can help you get the job with more ease. 

Here is what you need in order to create a perfect data scientist resume. In this post we will go through 8 steps to make your resume stand out for data scientist.

#1 - Get to Know the Job

Before you send your resume, read though the job ad of the company you are applying to. This will give you the right clues as to what should be on your resume - which skills, which achievements, which traits and so on. It will also help you see what are the appropriate keywords that their system is going to recognize. So, for example, if they use “highly skilled in machine learning” that’s the exact phrase you should use on your resume. 

#2 - Start with You

The first thing you’re going to want to do is to start by introducing yourself and including your relevant and best contact information, including your address, mobile number, email address, as well as links to your social media profiles, such as LinkedIn, if they’re relevant.

This is so the recruiter can tell exactly who you are instant without having to search the page, and they can identify your resume easily if they want to give you a callback.

#3 - Follow with a Personal Summary

The next thing you’re going to want to do is to follow on with your personal summary which is designed to give the recruiter an idea of who you are and why you would be good for the role. It’s important that this will be your first impression and will make or break whether they want to read on. Use this if you have a long work history. If, however, you are just out of school and have a small work history, start with career objective where you would say something like “Junior data scientist with 3 years of experience in freelance and project work. Built the models that fit the problem well and beat 200 data scientists and professionals with the solution. ”

Briefly talk about who you are, what you do and what type of person you are. Keep things short and concise, and this entire section doesn’t need to be longer than several sentences; five at most.

#4 - Talk About your Individual Skills

“The thing that lets most people down when it comes to their resume is that they write them too generically and they don’t showcase enough of the person as the individual they are. Instead, stand out from the crowd and be yourself” shares Nick Dwelling, a recruiter for Eliteassignmenthelp and State of Writing.

This means talking about your skills and what makes you a good data scientist. These skills should include being a strong communicator, collaborator, be creative and determined as well as detail-focused. 

This is another place to look at keywords the employer used in the job ad. For example, “collaboration, Python, Hadoop, R Programming, NoSQL, Open Refine, Data visualisation etc.”

#5 - Delve into Work History

The next most important thing a recruiter will be interested in is your work history. While it can be tempting to try and include everything in chronological order, you don’t actually need to do this. Instead, pick the most relevant job roles you’ve had and talk about them in detail.

If you’ve been a data scientists elsewhere, this is the first role you’ll want to mention since this is the first thing they’ll see. Talk about your role, your responsibilities, and what you achieved in your past roles. For instance, “Created and presented models for pottential holdings to fund managers, achieved 20% better results compared to past results, predicted stock price 25% better than traditional figures…”

#6 - Use Online Tools

Writing in a professional manner can be hard work, especially if it’s not something you’re used to, but this doesn’t need to hold you back when it comes to writing your resume. Instead, you can utilize online tools to help you get things right. Here are some to get you started;

Resumention / Studydemic - These two websites are full of writing guides and tools to help you perfect your data scientist resume.

Paper Fellows - An online editing tool to help maximize the readability of your resume.

Academ advisor - This is an online grammar checking tool to ensure you’re using grammar correctly.

Academized / Australian Help - These are two formatting tools to help you structure and organizing the contents of your resume.

My Writing Way - Use this tool to check plagiarism reports, and to help you proofread your resume accurately.

Big Assignments / Ox Essays - Use these two tools to help generate subheadings for your resume, and to generate catchy keywords relating to the data industry.

#7 - A Note on Education

Most recruiters will be far more interested in what work experience you have, rather than your education of years ago. However, if you’ve got a limited work history, perhaps having not been out of school long, or you have training and qualifications you want to share, make sure you include them.

#8 - Always Read Through and Check

Once you’ve finished writing your resume and you think it’s ready to send, make sure you read it through and check it again. Make sure all the information and dates are right, there are no spelling mistakes, and everything reads well.

Ideally, you might want to get a friend or family member to relook over your resume with a second pair of eyes to see if they spot anything you don’t.


As you can see, there are many points to remember when it comes to writing a resume when applying for an data scientist position. Make sure you’re not being generic and taking the time to write out a professional resume that really showcases who you are.

Author's bio:

This great article is contributed by Freddie Tubbs. Freddie is a career advisor at UK Writings and Essay Roo services. He also works as a business writer and contributor at Boom Essays writing company.

Comments and discussion
Have a doubt, write here. I will help my best.
Before commenting you must escape your source code before commenting. Paste your source code inside
<pre><code> ----Your Source Code---- </code></pre>