Some Reflections on Being Turned Down for a Lot of Data Science Jobs
I manage a data science team and revamped the hiring process pretty substantially about a year ago, to good results. Nothing in here is particularly original, but here&#x27;s what we do:<p>1. Break down &quot;data science&quot; into several different roles–in our case, Analyst (business-oriented), Scientist (stats-heavy), Engineer (software-heavy). Turns out that what we mostly want are Engineers-Analysts, so our process screens heavily for those.<p>2. Figure out which types of people can be trained to be good at those roles, given the team&#x27;s current skillset. I opted to look primarily for people with strong analysis skills and some engineering.<p>3. Design interview tasks&#x2F;questions that screen for those abilities. In my case, the main thing I did was make sure that the interviews depended very little on pre-existing knowledge, and a lot on resourcefulness&#x2F;creativity&#x2F;etc. E.g. the (2-hour) takehome is explicitly designed to be heavily googleable.<p>4. Develop phone screens that are very good at filtering people quickly, so that we don&#x27;t waste candidates&#x27; time. By the time someone gets to an onsite interview on our team there&#x27;s something like a 50% chance they&#x27;ll get an offer.<p>On the candidate side, when I&#x27;m applying I try to figure out first and foremost what a company means by &quot;data scientist&quot;, usually by networking &amp; talking to someone who already works there. This filters out maybe 90% of jobs with that title, and then I put more serious effort into the rest.