As someone who has had bad data science interviews before getting my current data science job, the process is highly variable. I've had interviews where the interviewer is looking for a specific right answer, with the answer being a binary you-know-it-or-you-don't thing that can't be talked through or worked out in a dialogue with the interviewer.
An example was a whiteboard problem requiring the BETWEEN syntax for SQL window functions, which is very uncommon. After I asked for a hint, the interviewer replied "You don't know the BETWEEN syntax for window functions? Everyone knows that."
My favorite iteration of this is also when the interviewer has a suboptimal answer to this question, and expects you to parrot the wrong thing back to them.
I could tell I annoyed my interviewer when they told me I was wrong and I demurred, and politely asked them to look it up since there was some question about the facts. They did not look it up.
I had that from a Microsoft interviewer. Thought my code was O(n^2) because he was a C guy...whereas I was writing it in Java (something I had checked would be okay with both the recruiter and the interviewer). Querying the length of a string is an O(1) operation, not O(n), so while you could make the case it's suboptimal (since a function call per loop instead of just a variable lookup), it's not quadratic in behavior. And when he asked what I would do if a number overflowed and I said "...let the exception bubble up because based on the function you asked me to write there is nothing I can do cleanly within it" it was pretty clear the interview was over.
An example was a whiteboard problem requiring the BETWEEN syntax for SQL window functions, which is very uncommon. After I asked for a hint, the interviewer replied "You don't know the BETWEEN syntax for window functions? Everyone knows that."