- Machine Learning (both 1st and 2nd round): covered a several topics on statistical learning, including both theoretical and practical issues. - Statistics: mostly driven by solving a few practical problems, and touched many key statistical concepts and methods in the process. - Programming: covered data structure, complexity estimation, as well as writing code to solve an interesting puzzle. The code was run and tested. - Deep dive: asked about previous projects in details, and also solving a modeling problem. There were questions that the interviewer did not expect you to fully answer, but you were expected to have a meaningful discussion on how to solve the problem with the knowledge you have.
