Data Scientist applicants have rated the interview process at Uber with 3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 100% positive. To compare, the company-average is 69.8% positive. This is according to Glassdoor user ratings.
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Take-Home Challenge, Technical Phone Interview, Onsite. The questions were very thought-provoking, and focused on applied modeling or inference problems that Uber faces. The interview questions gave me a good sense of problems they were working on. My interviewers were also very nice people. I had an excellent interview experience!
Interview questions [1]
Question 1
Applied modeling or inference problems that Uber faces
I applied through college or university. I interviewed at Uber (San Francisco, CA)
Interview
The interview process is smooth. Asked two questions related to Uber's data problem in the real world. One is about Uber eats and another one about Uber pool. The interviewer would discuss the problem with you.
I applied online. The process took 2 months. I interviewed at Uber (San Francisco, CA)
Interview
Contacted by recruiter, 2 different 1 hour technical phone screens (the second lasted only 30 minutes), and invited on site. On site interview consisted of 6 different 45 minute interviews and a summary with the recruiter.
Interview questions [1]
Question 1
The interviews, as I recall:
(1) Someone from finance department, covering collaboration w/ members outside of the core group.
(2) Hiring manager asked questions around behavioral and problem solving approach; critical thinking; fit for team and group.
(3) Product manager asked questions on product intuition and product impact. Being able to connect the problems you’re solving back to the business. Are you able to communicate with a non-technical audience. Some questions about metrics.
(4) Coding -- one data manipulation question using Python or R. Could have used SQL to answer it as well. One algorithm/data structure question which honestly isn't really relevant to what someone w/ a statistical modeling background does, but seems to be included these days nonetheless.
(5) Data science interview -- met with data scientist team leader who asked questions regarding how to define a given data science problem, what kind of metrics to use, models you'd consider using to answer such questions, etc. Some of the questions were experimental in nature, others were more machine-learning-esque. Of the latter category, would have been beneficial to at least be aware of online learning techniques and perhaps some time series models given the temporal nature of the data.
(6) Interview with person not on the same team as the group I interviewed with. This person was supposed to ask me to explain my research and projects and experience according to the recruiter, but wound up just asking me questions about their own work, unexpectedly.