Writing Impactful CV’s for Data Science roles

Your CV is often the first impression employers get of you. As with face-to-face meetings, impressions are created almost instantly, and you need show your relevance immediately.

If you’re not already working as a Data Scientist, you’ll have to work a little harder to evidence that you’ve started building the skills they require of you.

Here are some quick steps for making sure your CV passes the initial sift in the selection process:

1. Analyse your current CV like a bot

Whether or not your potential employer uses a resume bot for initial CV screening, it’s not a bad idea to check how well the CV maps to your target. A quick automated check to match your CV against the position you’re applying for can help identify gaps, and suggest ways of updating your content.

This won’t give you all the answers on how to reshape your CV for a Data Science audience, but it can be a starting point. Click here to analyse how your CV matches the job specs of your desired positions.

2. Build up Data Science-relevant content for a human scan-reader

The trick to building a CV for a career transition is to rebalance the amount of space you dedicate to relevant material. The reality is that your Data Science experience at this stage may be limited to training courses and projects, and while this may seem insignificant compared to actual work experience, the way you package your content is up to you — there are no rules.

Think about the UI of your document — the position of sections, the naming of section headings, and the good use of white space. These are the elements which can help orient the reader to the material you want them to see. The more space in vertical dimension you dedicate to a section on the CV, the more likely the reader will spend time looking there.

Remember that the humans are NOT processing the info like a bot simply looking for keywords. Your design can channel the reader most relevant content, and give an insight into your thought process on a project (i.e. why you chose a particular approach or tool, what was the impact of the model you developed and so on).

3. Create a significant technical skills section

A detailed and comprehensive section outlining technical skills is a must, and shouldn’t be left to the end of the CV. It should occupy a prominent position and have a good chunk of space dedicated to it. Not everyone starts out in Data Science with a programming background, but the stronger your skills the better, so make sure to talk about what languages, tools and techniques you’ve applied in the past, your level of proficiency in them and how you’ve used them.

You should also include your mathematical and statistical skills within a technical skills section when you’ve done mathematical modelling and algorithm-based problem solving. This will include any expertise you have in linear algebra, regression, probability theory, numerical analysis or Machine Learning methods. A quick summary of these skills in a visible spot will enhance the scan read.

4. Use projects to compensate for the lack of relevant work experience

If you’ve been involved in end-to-end projects as part of a course, bootcamp, or just in your spare time, then a detailed account of the process and the results would be advantageous — highlighting what you’ve learned and the techniques you’ve started to play with. Go into detail on what the projects involved, including bullet points on data collection, cleaning, methods of analysis, results and insight.

5. Consider a personal profile

Personal profiles — the optional summaries at the top of a CV — are often badly executed, full of unsubstantiated adjectives or simply content-free. However, they can help career changers if you focus on facts, keep it short, and clearly state your future aims. It’s a strategic way of including Data Science vocabulary from the very start.

Make it as factual as possible and of course mention any relevant Data Science training and projects you’ve done, plus the headlines on your previous work experience and education if appropriate. It can be difficult to summarise your skills and experience in effectively 2–4 lines, so if you try it and feel it doesn’t work, leave it out. If it reads well, then it can be a real help — and can be modified depending on who you’re writing to.

6. Think about your previous work history and education with new eyes

What is it about your previous work that a Data Science employer will find interesting? Did you work with data or analysis in a different capacity? Did your work involve problem solving and idea generating? Did you work different stakeholders to improve commercial or operational outcomes? Showing that you can work collaboratively and can use data for decision making and impact is an important aspect to stress.

With your educational background — even if it’s some while ago — you should stress any mathematical, statistical, and analytical work that you did, even if these were not your subjects. Did you do a project that involved data analysis or problem solving? If so, it would be worth mentioning.

7. Showcase a Data Science portfolio

Include your Github account, Kaggle or LinkedIn profile at the top of the CV, alongside your contact details if you think there is enough there to warrant it. Building a portfolio of Data Science material can where your code or your models can be seen will showcase your abilities and the tools you’ve been using.

The principles of good CV design apply for Data Science as well as anything else — good design to aid scan reading, well written content and an explicit matching of your skills to what your target is looking for. Data Science is an immature field with people transition into it from academia, software, quantitative finance and other routes, so the challenge is to translate your previous background for a new audience.

‘’The main piece of feedback we get from candidates looking for their first Data Science role is lack of practical and relevant experience to offer. As this article alludes to, if you can display and showcase any relevant project work from Kaggle or such like — even some from academia, that will most certainly help you stand out. Particularly if it’s interesting and a unique piece of work!’

- Dan Holdworth, Data Science and Analytics Recruitment expert at People Genius

Keep a lookout for a follow-up piece where we lay out other ways you can stand out from the competition when applying for positions employers, as well as things you may not have realised could give you an edge from other applicants.
Register for our bi-weekly Data Science and Machine Learning Newsletter. 

Data Analyst Apprenticeship L4

Learn advanced data analysis skills
with a government-funded apprenticeship
June 2020 start

Subscribe to our blog