How to write a great data science CV
By Simon Spavound and Abhinav Singh on May 20, 2021 - 5 Minute ReadAt Peak we have a great team of data scientists, which has grown rapidly from our first DS in 2016 to over 50 in 2021.
Even during the pandemic we have constantly grown our team into one of the largest in the north west (we’re still hiring, too – check out our careers page!) During this time, we’ve collectively reviewed thousands of CVs – it’s safe to say we’ve seen CVs in all different types, shapes and sizes. Some great, some not so great.
Given how new the field of data science still is, there aren’t many conventions on what an objectively good data science CV looks like. So, we’ve pulled together a few tips on what makes an effective CV, featuring some do’s and one don’t! In future posts, we’ll share advice on applying for your first data science position, as well as offering help to those entering the field from academia.
Do: Put your industry experience front and center
Given how young the field is, experienced candidates should be putting their industry experience forward, as commercial experience is highly desirable. In as much detail as possible (given the constraints of NDAs, etc.) explain what you have done and – crucially – what impact your work has had on an organization.
Is your dashboard used daily by your CEO to make impactful decisions? Has your analysis resulted in a better customer experience for your company’s customers? Did your work save your company a lot of money in efficiency savings? These are all great questions to think about to drive home the impact that your work has had.
Similarly, try not to focus on error metrics or accuracy too much – it’s much more impactful to say that you improved forecasting accuracy by 2%, which led to your company reducing its wastage of products by 500 tonnes a year!
In as much detail as possible, explain what you have done and – crucially – what impact your work has had on an organization.
Do: Expand on your personal projects
Junior data scientists often have personal projects which are a great way to showcase your skills (especially if you’e trying to land your first job in the field!)
However, try and make that experience unique to you. Lots of applicants have analyzed the Kaggle Titanic dataset and, in an increasingly popular field, your CV should stand out – have you got a favorite sport that is rarely analyzed that you could apply machine learning techniques to? Is there an interesting dataset that is openly available that no one else will have analyzed yet?
All of these are great ways of helping you stand out and get noticed by recruiters. Think carefully before you include a link to your Github profile – many reviewers will not have time to look at it, so all of the important details should be covered in your CV. If you do choose to include a link to your Github, think about what it will look like to someone else reviewing it.
Make sure that the Github link demonstrates your key skills – maybe by linking to a significant project you have worked on (make sure, if you’re trying to demonstrate something complicated, that it has a Readme or something similar to explain to someone what you have done!)
Having a Github profile strewn with a miscellaneous collection of projects is hard to navigate and doesn’t add to your application. These personal projects can be particularly good if you have gaps in your skillset which you think would be useful for the role (and show you are driven to equip yourself with the skills you need) but should become less prominent as your build up experience.
Interested in a career in data science?
Take a look at our careers page to browse our current vacancies.
Do: Discuss your responsibilities
Given the wide variation that now exists between data scientists in different companies, try to expand on your day-to-day. Do you spend most of your time talking to customers and understanding their problems? Do you put models into production yourself, or work closely with an engineering team that do so? Are you involved in deciding what models and techniques should be used? Are you building new algorithms to solve difficult problems?
These are all great things to think about to show what it is that you do. This is definitely preferred to lists of the techniques and technologies that you are using – listing Pandas/Scikit-learn/Tensorflow/Hadoop, but not actually telling us what you are doing with those things, doesn’t give the best impression of your wide skillset.
Don’t: Forget to answer any supplementary questions
Many application forms (including ours!) ask additional questions along with requesting a CV. For those recruiting for a role, these are vital pieces of evidence of how interested you are in the role, and how good a fit you might be. Don’t make the mistake of spending your time crafting an amazing CV and then not answering these key questions. They are one of the best ways to demonstrate your interest and focus on the role.
We hope that you’ve found these tips useful – if you have any questions please feel free get in touch with us.
We’re always looking for great new colleagues to come and join us in solving interesting problems for some of the world’s biggest companies. If this sounds like something you would be interested in, please apply here!
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Do I need a PhD to be a data scientist?
