Difficult statistical questions, much different than other data science interviews. Focused on theory on hypothesis testing, time series analysis, logistic regression. Also had a short coding exercise in python. Overall a hard interview process.
Interview questions [1]
Question 1
Derive the maximum likelihood estimator for logistic regression
The standard interview process starts with resume screening, followed by initial phone screening, technical and behavioral rounds, a final leadership interview, and ends with reference checks and a job offer.
In the technical round, I faced a challenging A/B testing question regarding YouTube thumbnails that pushed my analytical skills. I was also asked to discuss metrics for evaluating user engagement, which had me thinking on my feet. The behavioral section was tough, but I found the principles I studied on PracHub to be incredibly relevant. The overall experience was intense and demanding, yet I received an offer in the end. Ultimately, I decided to decline, as I felt it wasn't the right fit for my career goals.
Interview questions [1]
Question 1
Design an A/B test to evaluate the impact of a new YouTube homepage thumbnail design on user watch time. How would you choose the success metric, sample size, and handle novelty effects?
It was all good, the interviewer was very nice. Technical questions were a bit challenging but overall it was good. The hiring manager was looking for some hands on experience