A phone screen with the hiring manager who detailed a simple case study and asked some questions. Then I was scheduled for an SQL screening of 4-5 live coding problems which I did not pass despite thinking I did okay. The interviewer was extremely helpful and nice to such an extent that I found it deceptive given that he did not pass me. He said it was more important to get the correct answer than working through the problems at speed. I answered three problems correctly in the 45 minute time window. I was upfront that my SQL knowledge was basic, given that I use R for all my data manipulation. The hiring manager stressed that they gave Data Scientists autonomy to use whatever tools they were comfortable with, but then they followed that up with a very rigid interview of "you must know advanced SQL in an unrealistic time crunch". The rejection email was generic and gave zero feedback.

Spoke with recruiter. She was nice and it was a basic HR screen questions. Then spoke with, I believe Hiring manager. That's where my experience was so and so. First, he was late to the interview then it was supposed to be audio only Google Meet call, he started with video and I felt awkward to be interviewing with video off but I wasn't ready for video. I turned it on anyway.

Python portion of the interview was focused on LeetCode questions geared towards software engineers. The interviewer herself admitted as such. This should not be the case for a data scientist role. The Python questions asked of me were nowhere near what a data scientist would touch. The solution I provided to one question, reversing the integer 123 to get 321, was succinct and correct. Convert to a string, reverse, cast as int, and you get int('123'[::-1]), right? Wrong, because she wants you to do it without converting it to a string. Why, oh why, would one torture oneself like that? And what does this have anything to do with data science? The interviewer said this test "weeds out" candidates. As such, the only candidates that would advance to the final stage are software engineers with cursory experience in DS, not actual data scientists. Ironically, I was easily able to answer the SQL and case questions that actually pertained to the duties of a data scientist. ¯\_(ツ)_/¯