How To Write The Perfect Data Science CV

Roman Orac
Jul 14, 2021


These tips are also applicable to Software Engineers. Make a few changes in your CV and land that job!

Writing a good CV can be one of the toughest challenges of job searching.

Most employers spend just a few seconds scanning each CV before sticking it in the Yes or No pile.


Here are the top 5 tips that will increase the chances that your CV lands in the Yes pile.


1. Beautiful Design



Photo by Neven Krcmarek on Unsplash

Your CV should reflect your future potential.

I always thought a nicely designed CV is not that important — we aren’t designers so no one expects a polished CV from a Data Scientist, right?

Well, I was wrong!

A polished CV becomes important when applying for a remote position or a position in a bigger company which gets thousands of application.

Also read: 3 Ways to Get Real-Life Data Science Experience Before Your First Job

In Europe, we have a standardized template for CVs called Europass. While it has an online editor and surprisingly good user experience, the end result is not satisfying.


My 2 page Europass CV.

The boring design of the Europass CV doesn’t catch the eye. It doesn’t reflect your future potential.

A CV that catches the eye


I wrote my CV with Awesome-CV template.

You don’t need design skills to write an eye-catching CV.

Awesome-CV
is a beautiful CV template in which you only need to change the content. You can also personalize it by changing the colors to your liking.

This beautiful CV template is made with LaTeX — a high-quality typesetting system. LaTeX is the de facto standard for the communication and publication of scientific documents.

A prerequisite to creating a CV with Awesome-CV is to install LaTeX on your computer.

After you’re done with editing, you need to compile the CV with LaTeX and it will output a PDF.

2. Less is More



Photo by Prateek Katyal on Unsplash

Many applicants write a CV that spans over multiple pages.

Employers don’t have the time to review long-form CVs so many automatically put them in the “No” pile.

Also Read: How to Get a Job With Python

Don’t believe me?

Take a look at Joseph Redmon’s CV — the mind behind YOLO (Unified, Real-Time Object Detection engine).

It’s a one page CV that looks like it came from a cartoon. But it catches the eye.

I recommend writing a one-page CV as it forces you to condense your work experience
.

3. Optimize for Applicant Tracking Systems



Photo by Kelly Sikkema on Unsplash

Big companies like Google and Apple get thousands of applications for each job vacancy. Recruiters cannot efficiently review all of them, so they use Applicant Tracking Systems (ATS).

ATS is a software that scans and analyzes your CV. The recruiters only see the scorecard of your resume which is the end result of the scan.

It is important that your resume contains keywords related to the position that you are applying for.

Eg. Important keywords for a Big Data Engineer position are Big Data, Hadoop, Spark, Redshift, etc.

4. Put most significant achievements first



Photo by Fauzan Saari on Unsplash

Put yourself in the role of a recruiter. How would you review a CV?

Most probably you would skim the first few words of each bullet point.

Well, recruiters do the same.

Read Also: How to keep your skills sharp while data science job hunting

Make sure you list your most significant achievements first. Put tedious work last.

Don’t write about the work you did, instead describe the results you got
. Write about the business impact of your work.

Even better if you can quantify the work you did. Use sentences like “reduced the costs“, “automated processes”, “optimized”…

For example:
  • Initiated analytics tools to make data-driven decisions in the marketing department,
  • Automated management of the distributed nodes with statistical distributions,
  • Maintained and optimized Ad network platform to spend less on the Cloud infrastructure.

5. Don’t go too deep into details



Photo by Octavian Dan on Unsplash

When describing your projects don’t go too much into detail — list only the most significant details.

Describe briefly what the project is about, what problems it solves, mention interesting facts, like “our app was ‌the iPhone‌ Business App of the Month in the UK”.

Describe your role in the project, the challenges and the solution. Eg. Recruited to improve the accuracy of classification for one of the most popular business apps for self-employed.

Try to quantify the results: Categorization accuracy improved by 30% using a Machine Learning model.

Add a Tech stack with technologies that you’ve used: Python, sklearn, etc.

An example of a good project description.

Conclusion



Having a polished CV will increase your chances of being seen by the employer. You’re going to stand out from others.

Hopefully, these tips will help you land that job! Let me know in the comments if you would add any other tip.

Follow me on Twitter, where I regularly tweet about Data Science and Machine Learning.

“How To Write The Perfect Data Science CV”
– Roman Orac twitter social icon Tweet


Share this article:

0 Comments

Post a comment
Log In to Comment

Related Stories

Oct 16, 2021

6 Advanced Statistical Concepts in Data Science

The article contains some of the most commonly used advanced statistical concepts along with their Python implementation.In my previous articles Be...

Nagesh Singh Chauhan
By Nagesh Singh Chauhan
Oct 09, 2021

Top 10 Python Extensions for Visual Studio Code

In this new post we want to talk about the most useful Python extensions for Visual Studio Code. Visual Studio Code is an integrated development en...

Daniel Morales
By Daniel Morales
Sep 25, 2021

10 Highly Probable Data Scientist Interview Questions

The popularity of data science attracts a lot of people from a wide range of professions to make a career change with the goal of becoming a data s...

Soner Yıldırım
By Soner Yıldırım
Icon

Join our private community in Slack

Keep up to date by participating in our global community of data scientists and AI enthusiasts. We discuss the latest developments in data science competitions, new techniques for solving complex challenges, AI and machine learning models, and much more!

 
We'll send you an invitational link to your email immediatly.
arrow-up icon