Netflix Data Science Roles?
Which teams at Netflix hire data scientists? Which teams focus more on ML and which ones focus more on product and analytics? What are the typical TCs for each focus? What areas are tested in the interviews? I like to know how best to prepare for product/analytics role. My skills: Python/SQL above average, problem solving strong, plenty of experiece in data exploration and product development (HW). TC 210, PhD 15YOE
How is the interview difficulty for analytics engineer then? If it’s a glorified data analyst are they really just going to test you on sql/metrics (skills that a lot of people have or can quickly acquire) and hand you 350k? How do they filter people out for this role?
I doubt they would hand 350k just like that to anyone who knows some sql. To even get to the interview, they would probably screen people based on education and experience. If you already have an advanced degree with some years of relevant experience, your market value in the Bay Area is already above 2-250k. So yes even if the interview is not as challenging, it doesn’t mean anyone with some basic sql and data analysis skills can earn 350k. You need to possess the proper background first. Also, I have heard Netflix is notorious for firing people. I don’t know if this is mostly due to cultural fit or technical skills, or maybe both, but the point is even if an unqualified person somehow gets through, I would doubt they could survive there for more than a few months. Perhaps NFLX DS folks can chime in.
At Netflix we tend to define data scientist as one who does ML - as opposed to more broad definitions that might include non-ML engineering. The vast majority of these roles are in the Data Science and Engineering department. The engineering part of DSE is data engineering and analytics engineering. TC can be highly variable based on experience/impact.
Thank you. Am I to assume that Netflix does not have many non ML DS positions/engineers? What’s the WLB like for engineers at DSE? What skills are most desirable?
We have lots of non-ML engineers, but we don't call them data scientists. We call them Analytics Engineers (think BI professionals using Tableau/MSTR and writing SQL), and Data Engineers (think ETL in Spark). Then there are lots of SWEs working on the product as well as a multitude of internal products. WLB can vary between teams, depending on how under-resourced they currently are. My WLB is not bad.