I applied through an employee referral. I interviewed at Uber
Interview
Refer by an employee and got an interview two weeks later. A bunch of sql and stat fundamental questions. difference between join, explain p-value, CLT, assumptions for linear regression etc. Need to review basic fundamental questions for stat
Interview questions [1]
Question 1
explain p-value, CLT, assumptions for linear regression etc.
I applied through a recruiter. The process took 2 months. I interviewed at Uber (San Francisco, CA)
Interview
It was a Data Scientist position at the Marketplace Optimization Data Science team.
Contacted by recruiter. First a phone screen with a Senior Data Scientist going over problem solving, stats/math etc, followed by a take-home data science exercise, and finally invited to on-site with 6 back to back interviews.
Didn't get the offer as I was in a pretty bad shape during that day and should have asked to reschedule instead. The interview questions were among the most difficult and quantitative but if you practice your advanced probability and modeling you should do fine. The people at Uber were amazing and I can truly felt that I'll be working with a stellar team if I could've got in. The interviewers will grill you in every way possible but they were never hostile, always on point and reasonable. Everyone was professional from the recruiter to the hiring manager and the overall experience was pleasant.
The only downside is that the onsite schedule seemed a bit rushed with 6 back-to-back interviews with hardly any breaks in between, but probably also tells you how fast-paced Uber is.
Interview questions [1]
Question 1
How would you measure the performance of [some Uber business]?
Modelling and optimization problems
Advanced statistics
I applied through an employee referral. I interviewed at Uber (San Jose, CA) in Sep 2017
Interview
Directly talked to a team member from Uber eats for a data scientist position.
He made up question through my explanations. He raised business related questions to Uber. How to optimize model. How to measure evaluating regression.