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.
Here are the most commonly searched roles for interview reports -
one full-day interview on data analytics. interviewed with different departments. one session with one of the seniors was very challenging, we were not using some programming language and could not understand each other.
Interview questions [1]
Question 1
A case study. Uber asked me how I would solve the problem. I told them the way I'd construct the problem and the solution. but as feedback, they told me they were expecting a detailed plan, step by step, that I failed to do so.
Was given the wrong phone screen so didn't have time to prep for an unexpected technical interview. Still did decently.
Found out I didn't pass, got feedback - was docked points for not knowing a specific algorithm by name [though knew how to solve the problem], and also the interviewer misrepresented my answers [initially had the "wrong" answer after mishearing him say 0.9 instead of 0.99, walked him through my calculation for Bayes Theorem, corrected it after he clarified, and got the right answer, with his confirmation. But he wrote that I got the question wrong...?]
Recruiter was kind, interview was easy, yet somehow turned into a miserable experience because feedback was simply inaccurate.
Interview questions [1]
Question 1
[some simple application of Bayes Theorem]
["have you heard of XYZ algorithm?"]
I applied through a recruiter. The process took 2 weeks. I interviewed at Uber in Sep 2021
Interview
2 technical round, with python coding problem and machine learning case study; Each round for 1hour;
Coding round is about biased coin; ML round is about how to handle imbalanced data, how to choose metrics;
Interview questions [1]
Question 1
Coding round is about biased coin; ML round is about how to handle imbalanced data, how to choose metrics;