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Top tips for any data scientist seeking to revive their CV

29 Aug 2019

Mark Kelly of Alldus gives his expert advice on crafting the perfect CV for aspiring data scientists.

In order to get your resumé to stand out among the stack, there are a lot of things to consider. Even simple choices like what font to use – should you use bold and italics to set off your subheads, job titles, and other features?

Small details make the difference in the eyes of hiring managers, so creating that well-designed, standout CV could allow you to leapfrog your competitors and get you to the interview. Obviously, creating a functional data science CV is a little more complicated than just choosing a font.

The first thing to consider when submitting your CV is to know your audience. If you’re applying directly on a website for a position and the company is medium to large, it’s very likely that your CV will be subject to an Application Tracking System (ATS).

Tips for tailoring your CV to get past ATS

  1. Your CV will not be seen by a human being if it fails to get past ATS. So even if you are the most talented data scientist in the world, it won’t matter
  2. Don’t get fancy. Use standard fonts such as Arial or Calibri. Excessive formatting or decorative elements might present an unreadable mess to the ATS
  3. Make it keyword-rich, since ATS is looking for keywords specific to the job
  4. Target the right keywords. If you’re applying for a management position, you’re going to be scored on keywords that are relevant to qualities that are expected of a manager. Review your job spec to find suitable keywords
  5. Keep it simple. A boring CV that hits all those keywords is far more likely to get past ATS
  6. However, if you are emailing recruiters or HR personnel directly, then you will be able to get more creative with your CV

Use the right headings to grab a hiring manager’s attention

Here are the sections we recommend including on every data scientist resumé. A CV or resumé is never one-size-fits-all, so use these sections as you see fit.

Including both an experience and projects section will give the recruiter information they are used to seeing, but it also allows you to highlight specific things you’re really proud of working on.

Similarly, having a formal education section and a certifications section provides you with additional opportunities to showcase knowledge gained.

Resumé Summary or Objective

Who you are and what you’re looking to do.


This should be the focus of your CV. Remember to keep it recent and relevant. Don’t include work experience that is five or more years old, it is most likely irrelevant to high-end data science projects in 2019.

You should include your job title, the company, the period of time you held the position, and your accomplishments. If one of your past roles has more relevancy to the position you are applying for, then be sure to highlight your accomplishments more than your duties.


Obviously, if you are a recent graduate, then education will be the highlight of your CV. Remember to list post-secondary degrees only! If you’re a graduate, you can definitely go into greater detail in this section.


You can list any ‘micro-degrees’ in this section such as online courses or professional training, but again keep it relevant and recent.


Talk specifically about the skills that were listed in the job description. If the key skills are Python and R, then highlight your years of experience with these tools.

You can, if you want to, list your other skills further down. Do not list soft skills here, keep it strictly technical.

Additionally, don’t go overboard. A data scientist with two years of experience that lists more than six programming languages on their CV raises red flags.


One important factor to remember here is to focus on how your project solved a business problem.

Hiring managers don’t care how difficult the problem was or how cool the solution is, so keep that in mind when including projects on your resumé.


Highlighting any articles that you’ve written showcases your passion for data science.

With data science roles, you’ll need to interact with a variety of audiences, so it’s good to show you can explain ideas in a clear and efficient manner.

Hobbies and Interests

Only talk about your hobbies if they convey something about you. Don’t say, ‘I like travel’. Instead, say, ‘I have travelled to [x] many countries and am fluent in [y]’.

Crafting the perfect cover letter

My advice on this subject is simple. Less is more.

I find that the majority of my candidates are much better at writing code than cover letters anyway.

Another thing that you should try to avoid is following that ‘typical cover letter format’ you see plastered all over the internet. It will give the reader the impression that you are unimaginative and tired.

Obvious copy and paste jobs will just annoy the reader and give the impression that you’re lazy or don’t know what you’re talking about.

You see, there are lots of pitfalls to consider when writing your cover letter. As a recruiter myself with over 13 years of experience, most cover letters that I see are actually detrimental to an applicant’s success. In fact, I very rarely forward cover letters to my clients. Keep it simple with something like this:

Dear Hiring Manager,

I would like to apply to the position of Head of Data Science. My CV with detailed job experience is attached. The job description sounds really interesting to me as both fun and challenging. Meanwhile, [company name] seems like the perfect place for me to learn and further my career. Whenever you are free, I would love to sit down and have a chat about the projects I might be working on and the tools that are being used.

If you have any questions for me about my CV or otherwise, you can reach me by email or directly by phone.

Thank you for your time,


These kinds of cover letters are perfect when applying to a company in which you have no contacts, through Glassdoor or LinkedIn, for example.

But please, for your sake, do not use templates that you found on the first page of Google, ot you will end up sounding like a robot and companies don’t hire robots (yet!).

Again, this is not a one-size-fits-all example. If you are an avid writer and that is who you are, then write away. If you’re not, then just don’t!

By Mark Kelly

Mark Kelly is chief customer officer at Alldus. A version of this article originally appeared on Alldus’s blog.

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