Top tips on writing a CV for a data science job

Martin Pardey, Senior Business Director, Hays UK

Your data science CV is often your first chance to show off what you’ll bring to the hiring organisation. Working in data science involves taking information from a number of sources, before providing insights and solutions for the organisation or its clients. Whether at entry-level or in a senior role, it’s vital that the person reading your CV knows you’re comfortable doing this.

Below I’ve given some examples of what to include in your CV for a better chance of securing an interview. If you’ve already been put through to the interview stage, why not read my guidance on how to prepare?

Writing a CV for a data science job at a glance

  • Split your data science CV into sections – read more on that further down
  • Demonstrate your skills through examples in your employment history
  • Keep your points relevant to a data science job (If you’re struggling on this, I’ve discussed the role and the skills required in more detail here.)

What should you include in a data science CV?


  • Include a personal statement, or even cover letter if requested, that tells the reader in summary where you are in your career, what you can already do, and what you want to do next. Make sure it’s relevant to the role!
  • As with any data role, good presentation skills are likely to be required. Make sure that your CV is clear to understand and laid out to reflect your style of data presentation.
  • Set your CV out clearly and in sections. Put your achievements, key technical skills and chronological employment history in separate sections, rather than cramming it all into a timeline. This will make it easier for the reader to pick out the key information.
  • List your qualifications outside of your work, including any degree/thesis details.
  • Add a portfolio. Include relevant projects or publications, as well as any links to anything in the public domain.

Employment history

  • List your employment history in chronological order.
  • If you’ve had previous experience, don’t go into as much detail on the roles earlier in your career – your more recent achievements will stand out most.
  • For each role, list your main responsibilities or projects, and the impact they had. Let the reader know what you’ve brought to your previous employers.


What to do if you have no experience in data science

How can you get into data science if you have no experience?

  • Highlight any learning you have done around the missing items, such as courses or modules.
  • Point out the soft skills or technical skills you have that would be relevant. You can read more about what those might be here.
  • Prove that you’ve been capable of delivering useful information or insights in the past, or mention other projects and the positive effect they’ve had.

What to avoid when writing a data science CV

  • Don’t include too much information that isn’t relevant. It can be tempting to write about everything you’ve done if you’ve got a lot of experience, or discuss everything else if you’re starting out. Either way, keep your points aligned with the job spec.
  • Avoid using cliches that the reader will have seen repeatedly. If you want to show off your skills in a more effective way, I suggest reading this blog from our Global Head of Technology Solutions, James Milligan.

What you need to remember about CVs for a data science job

Tailoring your CV to the role is vital. List the qualifications and experiences you have in separate sections and demonstrate your skills through these. Following these steps will give you a much better chance of getting an interview for a data science role, whether you have no experience or are looking to step up the ladder.

Are you searching for a job in tech? Read our other CV tips here, or search our data science jobs here.


Martin Pardey
Senior Business Director – South East UK

Martin Pardey is a Director for Hays in the South East, the World’s largest specialist recruitment company. Martin has 15 years’ experience at Hays, specialising in the last few years in the Business Intelligence and Data Analytics sector.