Try looking for a company that does take-home assignments (these are mostly Kaggle-type problems). Then you don't need to learn formulas by rote, and the interview can focus on the assignment you did.

Experimental design (statistics) is important, but data science is a team sport. The people specialized in statistics may not be able to code up a deep net, and vice versa.

Computer scientists make great data scientists, especially if you know data infra. If I were to start a team from scratch, I'd focus on software engineers and physicists. Most are willing to pick up some ML to broaden their skills, and the ability to go end-to-end from conception to production is invaluable.

I'm sorry but a standard t-test is like the most basic thing in statistics after means and variances. It's not like they want you prove the CLT or something. To me it's more like a statistics fizz-buzz.

To which, of course, the correct answer is "Unless you have good reason to believe the data is normally distributed, you should be using Mann-Whitney", then explain that :-)

Thats not really true though. The vast majority of t-tests are done using a sample average as a test statistic, and by the CLT, as the sample size goes up, the distribution of a sample average becomes approximately normal. So unless you have a really weak sample size, the t-test assumption holds even in the absence of normally distributed data.

Excuse me, I think that at this juncture a Kruskal-Wallis would be more appropriate (assuming multiple distributions).

Thanks for sharing your experience!

Were you making it past the initial stage/screen interviews where every question is a buzz-word test? Were you ever interviewed by people who you'd actually work with and know what their team needs? What geographical area do you reside roughly, if I may ask?

it was initial screenings, she was a data scientist (but really a statistician). I passed the first two screenings (code interview, database / sql interview). every question was about statistics, and probability.

This position was in NYC.

For many companies, data science means statistics. Statistics are used for experiments and to analyze data. If you don't know what a t-test is, you cannot be hired as a statistician. However, you may be hired as a machine learning engineer.