Hays’ Martin Pardey breaks down the CV for budding data scientists and shares some top tips on how to get noticed by recruiters and employers.
Your 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. If you’re struggling on this, think about the skills required for a data science job.
Below I’ve given some examples of what to include in your CV for a better chance of securing an interview. If you’re ready for the next step, you can read further advice on how to prepare for a data science interview.
Writing a CV for a data science job at a glance
- Split your data science CV into sections
- Demonstrate your skills through examples in your employment history
- Keep your points relevant to a data science job
What should you include in a data science CV?
Include a personal statement, or even a 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.
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
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.
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 clichés that the reader will have seen repeatedly. If you want to show off your skills in a more effective way, read this article about properly highlighting your skills for at tech role.
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.
Martin Pardey is a director for Hays in the south-east of the UK. A version of this article previously appeared on the Hays blog.
10 things you need to know direct to your inbox every weekday. Sign up for the Daily Brief, Silicon Republic’s digest of essential sci-tech news.
