I think regurgitation of math formulas is a terrible way to hire for most data science positions. I've seen a breakdown of data scientists into two categories:

I think regurgitation of math formulas is a terrible way to hire for most data science positions. I've seen a breakdown of data scientists into two categories: 1) People who are great at the mathematics behind the statistical tooling 2) People who are great at conceptualizing a relevant question, operationalizing it, and then using a computer to apply appropriate models. I think in most cases, for businesses needing to solve business problems, the latter kind is probably more useful. There are applications where the former is required, but you probably know if you need this kind of data scientist. I should also add that these traits aren't mutually exclusive, but that individual data scientists typically are stronger or weaker along approximately those axes. In general, I still dislike the term "data science" because it obfuscates meaningful distinctions between math nerds, computer science nerds, and research nerds who happen to do some applied stats.