Uber·Data Scientist·Technical Phone Screen·Junior

JuniorPrefer not to say

Jul 2026Remote

Summary

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.

Questions Asked (1)

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.

Product Sense & IdeationA/B Testing & ExperimentationProduct Analytics & Metrics

Author's notes

This was basically the whole interview in one prompt.

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AI HintsAI Generated

Suggested Approach

Start by framing the feature as a solution to a specific rider or marketplace pain point, then structure your answer around a clear product narrative: target user, business value, metrics, tradeoffs, experiment design, and segment analysis. Emphasize how you'd rigorously measure impact and disentangle real effects from novelty and seasonality using control groups and statistical techniques.

Pro tip: Anchor your feature in Uber's marketplace dynamics—e.g., reducing wait times or improving driver utilization—and explicitly discuss how you'd use holdout groups and difference-in-differences to isolate true impact from noise.

1. Identify a High-Impact Problem

Choose a specific rider or marketplace inefficiency (e.g., long pickup times, price volatility, low driver utilization) and propose a feature that directly addresses it.

2. Define Target User and Business Case

Describe the primary user segment (e.g., frequent commuters, airport travelers) and articulate the business value: increased retention, higher conversion, or improved marketplace efficiency.

3. Select Success Metrics and Tradeoffs

List primary and secondary metrics (e.g., booking completion rate, ETA accuracy, driver acceptance rate) and discuss tradeoffs like cost, complexity, or potential cannibalization.

4. Design the Experiment

Outline an A/B test with a control group, randomization unit (rider or city), duration, and statistical power. Include how you'd handle network effects and interference.

5. Analyze Segments and Isolate Impact

Specify segments to analyze (e.g., new vs. existing riders, geographies, time of day) and methods to separate real impact from novelty (e.g., holdout groups, difference-in-differences, cohort analysis).

Key Points to Mention

Clear problem statement tied to a rider pain point or marketplace inefficiency
Target user segment and why they would adopt the feature
Business case with quantifiable impact (e.g., lift in retention or reduction in wait time)
Primary and guardrail metrics with definitions
Experiment design including randomization, control, and power analysis
Methods to control for novelty and seasonality (e.g., holdout, DiD, long-term cohorts)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.