loopasam

I've worked places of different sizes as data scientist (from start-up to mega-corp), and I've seen different ways to evaluate employee's performance (think yearly performance review). In my experience, it's ironically often difficult as a data scientist to demonstrate what you have or have not achieved during the year in a quantitative fashion. I would be curious to hear your input, what works and what doesn't in your opinion. Evaluation methodologies I have often seen are:

- Demonstration of impact on business: In this case it's up to the data scientist to justify as best as possible what business decision (or internal milestone) was made because of an analysis performed. In theory it makes sense (= your focus should be on impacting business), in practice I don't think I've ever seen a single analysis changing the course of anything; decisions are driven by many factors, your analysis being only one of them.

- Tool usage: I guess some programmers are evaluated the same way; basically, you develop a tool for co-workers to perform analyses with. The more the tool is used, the better it is for you (it's assumed that high usage = high business relevance). In this case the usage is sometimes easier to track and more impartial, but it's often difficult to develop a data science tool covering many use-cases, and one frequently ends-up with a niche product with low usage.