I applied online. The process took 1 week. I interviewed at Uber in Feb 2025
Interview
The process consisted of two screening rounds (one for SQL and another for Analytics Cases study/AB testing knowledge).
The first round went well, all SQL questions were promptly answered. The analytics case study consisted of a problem statement and how an AB test could be designed to solve that which i was able to solve and answer
Two days after that HR comes with back with a negative feedback . Asked if they can provide a detailed feedback to which the HR replies that received a simple no from interview panel (both rounds) and needs to follow up with the panel for a detailed feedback. I followed up multiple times with HR for the same to understand the gaps ( was highly surprised on the feedback received on SQL round) but no response. I have no idea what kind of expectations the interview panel had or if they were taking the interview rounds just to reject candidates. They need to value the time the applicant is putting in preparing for these rounds. The least they can do is to provide an actionable feedback for the candidate to introspect on.
Interview questions [1]
Question 1
Easy questions on SQL lag, lead , joins, dense rank, CTEs
Recruiter call to just check if you get how a two sided marketplace actually works and if you align with their core values. After that was a 45 minute live screen that was mostly advanced SQL window functions and some basic metric diagnostic questions. The onsite was a 5 round loop covering product sense, stats and experimentation, applied modeling, data processing, and a behavioral bar raiser. The stats and experimentation round is the real filter i think. You cannot just suggest a standard A/B test for a new feature. They really push you on network effects and driver cannibalization, so you have to know switchback experiments and synthetic controls inside out. Product sense was basically a deep dive into root cause analysis, like being asked to figure out why rider cancellations suddenly spiked in a specific city and walking through the exact metrics you would pull. The modeling round was less about writing math on a whiteboard and more about how you handle imbalanced data and pick the right tradeoffs for putting things in production. The bar raiser chat is pretty intense. They will dig deep into your past projects to see if you actually drove the business impact or just wrote the queries, and they care a lot about how you push back on product managers. For prep, do not just grind leetcode database questions. Practice structuring ambiguous product metrics and read up on their engineering blog. Doing a mock on prepfully with an Uber DS helped me to catch my blind spots with the switchback experiment stuff and get a reality check before the actual loop. Tough process overall but really engaging.
It was with a referral from a friend. Basic recruiter questions.
Call for tech screening - Python
I was hoping for Pandas type questions which I use in my day job. They asked about creating a function to get cumulative sum
Interview questions [1]
Question 1
I was hoping for Pandas type questions which I use in my day job. They asked about creating a function to get cumulative sum
I applied online. I interviewed at Uber in Jul 2025
Interview
Interview had DSA question about simulating probability distributions
Was asked about background while focusing on leadership, architecture & scale
Couldn't complete the question. I'd say the question was pretty tough.
Interview questions [1]
Question 1
Interview had DSA question about simulating probability distributions
