Most were pretty straightforward, but there was one that required some mental math on the fly that I flopped on.
Sr Data Scientist Interview Questions
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How do you prioritize under pressure? What projects?
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Recent projects, goals, timeline, work style.
1) Online technical test with MCQ and SQL coding exercise (matching the expected results is non-trivial and needs good effort) 2) Introductory Interview 3) Online Assessment on Loan Repayment Challenge
"What is the impact of your project, how do you measure success, and what are your next steps after achieving success?"
The 1st interview was a poorly framed problem, more like an 'open ended question'. It was related to graph databases and advanced clustering algorithms - don't think k-means, definitely way more advanced than that! The 2nd interview was with an engineer, and that involves solving a problem they have in reality and you are meant to propose a full end-to-end solution - again totally unrealistic to expect this when they haven't even solved this problem themselves! By the 3rd interview I was sure I didn't want to work there - the interviewer wasn't motivational in any way. She then moved hastily to a 'measurement' question which was technical too - how to measure whether an Ad was successful or not in a very specific scenario.
high level discussion on ML, data mining and A/B testing
Why MeshAI, why I choose x model, and why I evaluated in such a way. Questions about my approach, and business context about my solution (reducing FP/FN implications). Experience with generative AI. Any projects with difficult stakeholders.
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