Uber Interview Questions — Page 28

Uber·Data Scientist·Technical Phone Screen

May 2026

Uber data science interview with a pretty brutal causal inference question about ride-sharing experimentation. The kind of question that separates people who've read about IV from people who've actually used it under pressure.

A standard A/B test isn't possible for a new ride-sharing feature because of interference between units. Walk through how you'd use instrumental variables to estimate the causal effect on trip volume, including the full set of IV assumptions, at least two concrete instruments, the first- and second-stage regression equations, and how you'd diagnose weak instruments, test overidentification, handle clustering, and assess exclusion restriction violations under marketplace spillovers. Does an effectively unlimited supply environment make the exclusion restriction stronger or weaker?

“This one wrecked me a little.”

View Post

Uber·Software Engineer·Online Assessment (OA)

May 2026

Took the Uber Data Analyst Intern online assessment and pretty much fell apart on the SQL section. The MCQs were fine but I blanked on window functions mid-test and ran out of time on the second SQL problem. Not expecting to move forward.

SQL window function problem requiring PARTITION BY and ROW_NUMBER to rank or filter records.
A second SQL problem that required multi-step query logic under a time constraint.

“I knew what the question was asking, sort of, but the syntax just evaporated from my brain mid-test.”

View Post

Uber·Product Manager·Onsite - Product Sense / Strategy

May 2026

Prepped for a PM role at Uber and got a product strategy question about display advertising. Short session, not a lot of detail to go on.

Why are display ads considered ineffective or problematic?

“I went straight into user experience issues like banner blindness and irrelevance, then pivoted to advertiser ROI concerns.”

View Post

Uber·Software Engineer·Online Assessment (OA)

May 2026

Uber SWE online assessment, part of what looked like a Hack2Hire pipeline. One coding problem about connected components on a 2D grid. Pretty standard union-find territory but there's a sneaky edge case in the distance comparison that I almost got wrong.

Given a list of ball positions on a 2D grid and an integer d, find the minimum number of triggers needed to absorb all balls, where two balls connect if their Euclidean distance is strictly less than d, and connections are transitive.

“This is basically a connected components count dressed up in a physics metaphor.”

View Post

Uber·Software Engineer·Onsite - Coding / Algorithms

May 2026

Uber SWE loop with a phone screen and onsite, both heavily focused on meeting room scheduling problems in different forms. The stateful OOD version was the main event, but they also run the classic LC 252/253 variants as warmups, so you really need all three cold.

Design a meeting room booking system: given a fixed list of room IDs, implement bookMeeting(start, end) that finds an available room with no overlapping booking, records it, and returns the room ID (or signals no availability).
Follow-up to the booking system: implement cancelMeeting(roomId, start) to remove a specific booking, and lastNScheduled(n) to return the most recently created N bookings across all rooms.
Concurrency follow-up: how would you handle two clients trying to book the same room at the same time?
Given a list of meeting intervals, determine whether a single person can attend all of them with no overlaps (touching endpoints are allowed).
Given a list of meeting intervals, find the minimum number of rooms required to schedule all of them without conflict.

“This is the main course.”

View Post

Uber·Software Engineer·Technical Phone Screen

May 2026

Uber SWE interview with a pipeline throughput optimization problem. The core trick is binary searching on the answer rather than trying to allocate budget greedily, which I didn't see immediately and cost me some time.

You have N services in a pipeline where total throughput equals the minimum throughput across all services. Each service can be scaled up at a cost, and scaling a service multiplies its throughput. Given a budget, how do you maximize the pipeline's overall throughput?

“Spent the first few minutes trying to think of a greedy approach, like always scaling the cheapest bottleneck first.”

View Post

Uber·Software Engineer·Technical Phone Screen

May 2026

Uber SWE interview with a tree edge reversal problem. Classic graph question but the constraints push you toward an iterative solution which is where I started second-guessing myself.

You're given a directed graph with n nodes and n-1 edges that forms a tree when you ignore edge directions. Given a root node, find the minimum number of edge reversals needed so every edge points away from the root.

“Spent the first few minutes just making sure I understood the problem correctly, drew out the example on the whiteboard and traced through it manually.”

View Post

Uber·Data Scientist·Technical Phone Screen

May 2026

A data science interview at Uber focused almost entirely on one big scenario around ETA for the ride-hailing product. The questions went deep fast, covering causal inference, experiment design, and supply-demand dynamics. Not a typical metrics round.

What driver-level, rider-level, and platform-level factors can influence the ETA shown to a rider?
From a product management perspective, why does ETA accuracy matter as a metric?
You notice that longer ETAs are positively correlated with higher ride conversion rates. What internal and external explanations might account for this?
Design an experiment to measure the causal effect of ETA on rider conversion.
How would you choose the switchback window length in a switchback experiment, and what are the trade-offs between shorter and longer windows?

“Started strong on driver stuff (location, acceptance rate, traffic patterns) but stumbled a bit on the platform side.”

View Post

Uber·Software Engineer·Onsite - System Design / Architecture

May 2026

Uber system design round for a software engineer role. The whole session was basically one big question about batch processing pipelines and serving precomputed results at scale. Pretty deep dive, they kept pulling on threads.

Design a batch pipeline that computes the most popular food items per restaurant from order history, then exposes an API for clients to query those results.
How would you design the getTopItems API, including pagination, filtering by category or price range, and multi-tenant access control?
Why use offline batch processing here instead of a real-time streaming approach? What are the trade-offs?

“This is a meaty one.”

View Post

Uber·Data Scientist·Technical Phone Screen

May 2026

Uber data scientist interview that went deep into experiment analysis. The whole session was basically one extended stats question about how you'd actually run and interpret an A/B test end to end, not just the setup but the messy aftermath.

Walk through how you would analyze an A/B test after it ends, specifically how you handle the difference between session-level and user-level analysis.
How would you handle ratio metrics and skewed outcomes in an experiment, and what methods would you use to get valid standard errors?
Some users in your experiment never actually engaged with the treatment. How do you estimate the effect on users who actually received it, and what's the statistical framework for that?
Your experiment returns a large p-value. How do you decide whether that means there's no effect, and what tests would you run?
How would you use pre-experiment data to reduce variance in your experiment results?
If you want to look at heterogeneous treatment effects across subgroups, how do you control for multiple testing?

“This tripped me up a bit because I started talking about sessions and they pushed back fast.”

View Post