Uber Data Scientist interview questions

78 Data Scientists have shared notes from their Uber interviews, with the questions they were asked in each round. That is 13% of the 609 Uber interview notes on Screna AI.

78 Data Scientist interview notes

Uber·Data Scientist·Technical Phone Screen

Jul 2026
Uber data scientist interview with a pretty brutal causal inference question about a messy real-world A/B test. The kind of problem where you think you know the framework and then realize halfway through that there are like three simultaneous complications to untangle.
  • You ran an A/B test on surge recommendations sent to drivers, but 30% of treated drivers ignored the suggestion and surge in one zone spills over into neighboring zones. How would you estimate the causal effect on completed trips, and what analysis plan would you use to handle both the noncompliance and the interference?

“This one took me a minute to even parse.”

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Uber·Data Scientist·Onsite - System Design / Architecture

Jul 2026
Uber DS interview focused entirely on a deep system design problem around ETA prediction. The scope was massive and I felt like I was constantly playing catch-up trying to cover all the angles they wanted.
  • Design a full end-to-end system to predict pickup and drop-off ETAs at the time a ride is requested. Walk through your data sources, modeling choices, uncertainty handling, training and serving infrastructure, evaluation strategy, and how you'd roll it out.

“This was basically seven questions duct-taped into one.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
Uber data science interview with a meaty end-to-end design question about the map search experience. One question, lots of surface area, and I definitely underestimated how deep they wanted to go on the ranking and experimentation side.
  • Design the full product and data science system behind a map application's address search bar, covering candidate generation, ranking, handling of fuzzy inputs and typos, location-aware results, relevance and popularity signals, metrics, experimentation, and failure modes.

“This question is deceptively wide.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
Product sense interview for a Data Scientist internship at Uber. One big open-ended question that basically asked me to design a new rider feature from scratch, including metrics, experimentation, and marketplace tradeoffs. Pretty intense for an intern role.
  • Given that the Uber rider app already has core features like booking, ETA, fare estimates, and scheduling, propose one genuinely new feature that improves rider experience or marketplace efficiency. Walk through the target user, business case, success metrics, key tradeoffs, how you'd run an experiment, what segments you'd look at, and how you'd separate real impact from noise like novelty effects or seasonality.

“This was basically the whole interview in one prompt.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
SQL-heavy technical screen for a Data Scientist role at Uber. Two tables, one prompt, and a pile of edge cases that made me realize I'd been sloppy about age calculations for years.
  • Given a drivers table and a trips table, write a query to return driver names whose average fare across completed trips is strictly greater than $10.00. Exclude canceled trips and null fares. Output driver name and average fare rounded to 2 decimal places, ordered by average fare descending then name ascending. Drivers with no completed trips should not appear.
  • Using the same schema, count completed trips grouped by driver age bands (20 to 35 and 36 to 45) as of a specific reference date. Age must be calculated accurately using the actual calendar, not a 365-day approximation. Ignore drivers outside those two bands.

“Felt pretty confident here.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
Uber DS technical screen, two parts back to back. The SQL problem was gnarly and the Python section had a twist I didn't fully see coming. Felt okay leaving but not confident.
  • Write a SQL query to compute 48-hour unique CTR for a specific campaign broken down by variant. Deduplicate sends to the earliest send per user per campaign on or after a given date, exclude internal user accounts, and define CTR as distinct users with at least one click within 48 hours of their send time divided by distinct users sent. Return campaign, variant, send counts, unique clickers, and CTR. Also return a single row showing the absolute lift between test and control, and optionally include the counts needed to compute a 95% confidence interval for that lift downstream.
  • Given a pandas DataFrame with true labels, predicted probabilities, and sample weights, write code to: plot the precision-recall curve, find the threshold that maximizes weighted F1 using the sample weights, and compute precision at the top 1% of the population by predicted score with deterministic tie-breaking.
  • Explain why a model can have a high ROC-AUC (around 0.86) but a very low PR-AUC (around 0.18) at 1% class prevalence. Which metric would you optimize for a marketing click-through rate use case?

“This one took me a while to untangle.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
Uber data scientist interview with a meaty experiment design question that goes pretty deep into stats and product thinking. One question but it has about seven sub-questions packed inside it, so budget your time accordingly.
  • Design an experiment to test whether offering a bonus (cash or queue priority) increases the rate at which paperwork-complete candidates take their first action within 14 days. Cover eligibility rules, randomization unit, treatment arms (control, bonus upfront, bonus after first action, staged), stratification by motivation type, primary and secondary metrics including guardrails for quality and fraud, how you'd handle noncompliance and spillover, stopping rules, and when to roll out broadly vs. target narrowly based on heterogeneous effects. Also compute the minimum per-arm sample size to detect a 3 percentage point lift from a 20% baseline at 90% power and alpha 0.05 using a two-proportion z-test, showing the formula and a numeric answer.

“This one is basically five interviews in a single prompt.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
Uber data science interview that went deep on experimentation stats, way deeper than I expected. Four questions total, all connected to a single cancellation rate scenario, and they kept pushing on edge cases I hadn't thought through.
  • Given before/after cancellation rate data from a product change, how would you set up a hypothesis test for the difference in rates and compute a 95% confidence interval? Walk through your null/alternative hypotheses and what the interval actually means in practice.
  • How would you specify a logistic regression to adjust for confounding by city and hour-of-day, including an interaction term you think matters? Write out the model formula, state your key assumptions, and explain how you'd check calibration and overdispersion.
  • Calculate the per-arm sample size needed to detect a 0.5 percentage point absolute change in cancellation rate at 95% confidence and 80% power. Then factor in an intraclass correlation of 0.02 at the driver level: what's the design effect and how does the required sample size change?
  • Describe a stratified bootstrap procedure to build a confidence interval for the cancellation rate difference across cities. When would you prefer this approach over a normal approximation?

“This felt like a warmup but I overthought the test choice.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
Uber data scientist interview, stats-heavy technical screen. The whole thing was basically one long hypothesis testing question with a few layers stacked on top of each other. Not a vibe-check round at all.
  • For an email marketing A/B test comparing click-through rates between a control and a test subject line, how would you set up the null and alternative hypotheses, justify using a z-test via the Central Limit Theorem, and derive the sample size needed to detect a 2 percentage point lift with 80% power at a 0.05 significance level?

“This is three questions dressed up as one and I didn't realize that until I was halfway through answering the first part.”

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Uber·Data Scientist·Technical Phone Screen

Jul 2026
Uber data scientist interview that went deep into experimentation design. Four-part question on A/B testing that covered everything from sample size math to causal inference with DAGs. More technical than I expected for a single session.
  • Given a signup funnel A/B test with a baseline conversion of 5%, a target relative lift of 10%, daily traffic of 50k with 60% targeting match and 90% bucketing success, a 1:1 split, alpha 0.05, and 80% power: compute the required sample size per variant and the expected calendar duration to reach it. State your formula and variance assumptions.
  • For a proportion-based primary metric in an A/B test, would you use a Z-test or a T-test, and why? When do their results meaningfully diverge? Also address how estimating variance from data, small-sample corrections, and unequal group sizes affect the choice.
  • If a product stakeholder wants to stop the test early whenever results look promising, how would you design a sequential testing or alpha-spending framework to control Type I error? Describe the stopping rules and how they affect nominal sample size and expected test duration.
  • If randomization isn't feasible for a related rollout, sketch a causal inference plan: draw a DAG to identify confounders, propose an identification strategy such as difference-in-differences or instrumental variables, and list the key assumptions you'd need to defend and how you'd validate them.

“I knew the pooled proportion formula but fumbled the traffic adjustment.”

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