Perfect your Data Science CV with this helpful guide
The demand for data scientists is rapidly increasing as companies come up to speed with the fourth industrial revolution. The industry is expanding at an exponential rate meaning the term data scientist is becoming ever more ambiguous. This means it is time to move away from the buzz words and start showing hiring managers the impact that you have had.
It’s time for a new resume.
Moving away from the classic resume style that lists ALL of your responsibilities and ALL of your areas of input allows you to really hone in on why you’d be a great fit for the company you are applying to, by tailoring your experiences and projects. Hiring managers and companies aren’t immune to the ambiguity of the industry, they need to know where your expertise lies and how your mathematical genius can have a direct impact on their team, their business and work stream.
Here’s our guide on how best to translate your experience into impact.
Job Title, Company
List your projects, in order of most relevant to the position you’re applying to. E.g. if you’re applying to a reinforcement learning position, list these projects first.
-Project name, duration
-The task or problem you tackled, project duration.
-How you approached it (workflow),
-Your contribution (e.g. individual contributor, lead a team in SCRUM style, etc), the technical details (frameworks/languages)
-Business impact/outcome/success – quantify this where possible, e.g. % lift on sales.
Machine Learning Engineer, Google
Mountain View, 2014 – now
Hired as a Machine Learning Engineer on the Natural Language Processing team to improve the summarization function within Search.
It’s incredibly important to include your technical skills, ability and knowledge to carry out specific tasks asked of the job you’re applying for. Make sure you highlight what programming languages you use, coding experience and the knowledge of multiple programming languages will help show how strong of a candidate you are.
Think of your formatting as above when listing these skills, putting emphasis on the most relevant and your most advanced.
It’s important to consider when applying for a job, do not blanket apply to every data science role you come across. Personalise your resume for each type of job you are applying to, not all data scientists are created equally.
Include links: Git Hub for engineers, Google Scholar for Researcher incl. h-Index, number of citations.
Make your resume is relevant to the role you are applying for and condense and reduce any detail that doesn’t directly relate!
Some of the most popular layouts we see:
- https://www.latextemplates.com/template/moderncv-cv-and-cover-letter (without a photo)