Real, anonymous reports from people who interviewed for Data Scientist at Uber, newest first and distilled into what to expect across the loop.
My first step was a timed test with 17 questions over 60 minutes—two SQL questions at a medium level and fifteen aptitude questions. The aptitude section felt relatively straightforward and I could’ve finished it in about half the time, but I spent more of my clock on the SQL since I didn’t fully solve one of the problems. I ended up turning in what I had with that lingering feeling that the SQL was the weak spot.
My interview experience was essentially centered on a risk-focused SQL moment. I was asked to handle an SQL question, and then I worked through a confusion-matrix style question as well. I felt like I understood what I was doing, at least technically.
The interview process typically begins with a recruiter call that assesses cultural fit and confirms the candidate's background. This initial interaction sets the stage for the subsequent technical and behavioral evaluations.
Candidates undergo a technical assessment that often includes multiple SQL questions, with a strong emphasis on both basic and advanced SQL concepts such as window functions and joins. Performance in this round is critical, as it directly relates to the core responsibilities of the role.
Following the technical assessment, candidates typically engage in case studies that evaluate their analytical thinking and problem-solving skills. These case studies often require applying SQL knowledge to real-world scenarios, such as A/B testing or fraud analysis.
Behavioral interviews are a significant part of the process, focusing on how candidates handle ambiguity and work within teams. These interviews assess alignment with Uber's values and the candidate's ability to communicate effectively.
The interview process is structured into multiple rounds, often including back-to-back sessions that can be mentally taxing for candidates. The overall difficulty is described as average to high, with particular emphasis on technical precision and analytical depth.
Candidates often report a disconnect between their perceived performance and the final outcomes, with many not receiving offers despite feeling they performed well. This suggests that evaluation criteria may extend beyond technical correctness.