Technical interviews asked python and SQL questions. Generally SQL questions involved doing some joins and groupbys. Python questions typically had you read in data and then calculate some quantities. Sometimes they let me use Pandas and other times they had me basically re-create a pandas function which seemed a little strange. Was asked a few specific questions about time-series modeling due to the position.
What metrics would you use to measure the success of a new product launch? How would you calculate churn rate, and what key metrics would you monitor? If tasked with improving user engagement, which metrics would you focus on and why?
Here's an ugly data set. Do exploratory analysis. Create visualizations. Discuss. Build a predictive model with different approaches, concerns,validity, performance...
Describe how regularization works for linear models
Describe a time when you encountered an obstacle. How did you deal with it?
The interview questions were confidential.
Basic business case, how would you approach the problem, what data would you use, what model, how would you validate it
Use cases for product matching at Wayfair, what data would you use, what model would you build, what metrics would you use to assess model performance, how to scale model training for large amounts of data
Technical Case study on Ad revenue
Case study and SQL. Cannot disclose the details due to NDA.
Viewing 1941 - 1950 interview questions