7 heuristics used to quickly screen your Data Science CV: Prior experience as a Data Scientist, Business-oriented achievements, Business-oriented achievements, Layout / visual appeal, Machine Learning variety, Tech Stack, Projects
Managing Riskified’s Data Science department entails a lot of recruiting — we’ve more than doubled in less than a year-and-a-half. As the hiring manager for several of the positions, I also read through a lot of CVs. Recruiters screen through a CV in 7.4 seconds, and after recruiting for several years my average time is pretty fast, but not that extreme. In this blog, I’m going to walk you through my personal heuristics (‘cheats’) that help me screen a resume. While I can’t guarantee that others use the same heuristics, and different roles will differ in the importance of each point, paying attention to these points can help you conquer the CV screen stage. Additionally, some of these heuristics may not seem fair or could potentially overlook qualified candidates. I agree that talented Machine Learning practitioners who don’t invest in their CV could get rejected with this screen, but it’s the best tradeoff considering the time. Remember, a highly sought after position may attract a hundred or more CVs. If you want an efficient process, the CV screen has to be quick.
Here are the 7 heuristics used to quickly screen your Data Science CV:
I’m going to quickly run through your CV to look at your previous positions and see which are marked as ‘Data Scientist’. There are some other adjacent terms (depending on the role I’m hiring for), such as ‘Machine Learning Engineer’, ‘Research Scientist’ or ‘Algorithm Engineer’. I don’t include ‘Data Analyst’ in this bucket as the day-to-day work is typically different from that of a Data Scientist and the Data Analyst title is an extremely broad term.
If you’re doing data science work at your present job and you have some other creative job description, it’ll probably be in your best interest to have your title changed to a Data Scientist. This can be very true for Data Analysts who are de facto Data Scientists. Remember, even if the CV contains descriptions of the projects you’ve worked on (and they include machine learning), a title other than Data Scientist will add unnecessary ambiguity.
Additionally, if you’ve undergone a data science bootcamp or full-time masters in the field, this will probably be considered the beginning of your data science experience (unless you worked in a similar role earlier, which will warrant questions at a later stage).
Ideally, I’d like to read what you did (technical aspects) and what the business outcome was. There’s a lack of technically savvy data scientists who can talk in business terms. If you can share the business KPIs that your work impacted, that’s a big thumbs-up in my book. For example, indicating your model’s improvement in AUC is alright, but addressing the conversion rate increase as a result of your model improvement means you ‘get it’ — the business impact is what really matters at the end of the day. Compare the following alternatives depicting the same work with a different emphasis (technical vs business):
a. Bank loan default rate model — improved model’s Precision-Recall AUC from 0.94 to 0.96.
b. Bank loan default rate model — increased business unit’s annual revenue by 3% ($500K annually) while maintaining constant default rates.
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