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How to Nail your Data Science CV

Aspiring data scientists often see their CV as a mandatory piece of any job application that you write once and can then use and reuse multiple times. But they often overlook how important they are to pass the first filter of any recruitment process and make some mistakes that end up costing them job opportunities.

Here we list some key advice to make sure you won’t repeat those mistakes in the future.

Your CV’s only purpose is to get you an interview.

One might be tempted to have a lengthy CV as a way to display a very strong profile. But you should remember that, instead, your CV should be just clear enough to spark interest in the eye of the recruiter and get them to want to know more about you.

Job post’s keywords are generally (…) listed in the job description. Having them in your CV is definitely a plus, if not a must.

It does not have to cover everything about you. The more you write, the more likely it is to get confusing. Stick to the essential and make sure you find a condensed way to showcase what are your strengths and why people would like to hear more about you. Having clear communication is often a strong selling point for a data scientist, so make sure your CV conveys a clear message as well.

Photo by Ben White on Unsplash

Nobody reads your CV.

Ok, that’s not true. But nobody will read it thoroughly. Multiple studies stated that recruiters only spend 6 to 7 seconds reading a CV. Rather than reading it, they will just skim through it looking for keywords. These keywords are generally defined in collaboration with the hiring manager and are listed in the job description (now you get why it is important to read it carefully 😉 ). 

Recruiters are usually not technical persons although they develop an understanding of the different technologies and methodologies their colleagues use. So they won’t spend time trying to interpret your CV and try to remember whether Keras is a deep learning framework or a pokemon. If the job post mentions multiple times that experience with Deep Learning is needed, just make sure that “Deep Learning” is written plainly in your CV (and then you can give examples of algorithms and frameworks you are familiar with).

Having such a limited attention span from recruiters means you have to adapt the structure of your CV and deliver the most important information efficiently. A common recommendation is to have the main information visible in the first half of the first page and not buried deep down the first or second page of your CV.

On the last note, there often are debates on whether you should stick to a one page CV or could allow yourself to write two pages. Although there might be some local and cultural differences, a two pages CV isn’t synonymous with automatic rejections and many people landed a job with a two page CV. However, if you use more than one page, make sure the main information is accessible on the first page, ideally in its first half. Recruiters might check what is on your second page but they will likely not do it if the first page did not give them the motivation to check further.

It is also important to determine what makes your profile a strong fit for the position:

  • Is it because of the methods you master?
  • Is it because of your past experience in similar roles?
  • Is it because of your capacity to deliver projects?

Depending on what is the answer to those questions, you will choose to first list what is the most relevant, e.g.:

  • your skills if you are an expert with a particular technology listed in the job description (ideally with examples),
  • your experience if you have held similar roles in different companies and represent a sure value for the position,
  • or your main achievements if your past experience is not the most relevant but has done very well in slightly different positions in the past.

Each new application requires a tailored CV.

This does not mean you have to rewrite your CV from scratch but rather that you should adapt your CV to each job description. As mentioned before, there is a lot of information to be extracted from a job description such as the technical skills required or the type of projects you are expected to work on. Also, the structure of your CV is important and you should find the structure that best showcases your strengths for a given position. Sometimes it will be your past experiences, sometimes your core skills.

A strong recommendation is to have the main information visible in the first half of the first page and not buried deep down the first or second page of your CV.

There is a straightforward process you can follow for each of your applications:

  • Start from a base CV that lists your skills, past experience, education and achievements.
  • Identify the main keyword in a job description. Is the focus on statistics, coding, statistics,…?
  • Identify what makes you a good candidate for this specific job. Have you worked on similar projects in the past? Do you have proven experience with one of the tools they listed?
  • Write a small introductory paragraph about yourself highlighting your main strengths for this specific role.
  • Choose what to show right after this paragraph in your CV, e.g. your core skills or your experience. You can mix both by listing your experience and the tools you used in each of your roles if both your past experience and technical skills are relevant for the job.
  • Make sure that the keywords you identified as most important are easily visible. They should be listed in the first half of the first page, not buried in a long list of skills and eventually in bold.
  • Ditch unnecessary information. You might have done a nice project in PHP when you were young but if that’s not mentioned anywhere, it might be better to have a shorter and clearer list of skills than a long and confusing one. 
  • Get someone to read it. And ask them: 1) if they see spelling errors and 2) what they got are your main selling points, from this specific CV.


Sending a convincing CV does require some work but it is the key to being noticed by the recruiter. Sending the same generic CV to all job openings, ranging from data engineers to deep learning research, will generate frustration as it is likely nobody finds it compelling.

Recruiters are usually not technical persons (…) So they won’t spend time trying to interpret your CV.

Remember it is better to send 10 high-quality applications than 100 generic ones. So, next time, make sure that:

  • you identify what you are your strengths for the specific position you are applying to.
  • your CV reflect these strengths by highlighting the most relevant experience or skills.

Oh, and did we mention you should also write a cover letter?


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