How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a easy difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Uber.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Uber during technical interviews.

Design an Uber feature and analyze safety

Quick Overview

This question evaluates a data scientist's product-sense and analytical competencies, including feature ideation, metrics definition and validation, randomized experiment design with marketplace interference considerations, trade-off analysis across rider/driver/safety/revenue dimensions, and time-series and causal inference methods for safety trends in a ride-sharing marketplace. Commonly asked in analytics & experimentation interviews, it assesses both conceptual understanding and practical application by testing metric literacy, hypothesis generation, experiment power and randomization choices, and the ability to distinguish true safety changes from reporting or measurement artifacts.

Company: Uber

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: easy

Interview Round: Onsite

You are interviewing for a Data Scientist summer internship at a ride-sharing marketplace. Part A: Product case Uber wants ideas for a new rider-facing or driver-facing feature, or a major improvement to an existing workflow, that would make the app meaningfully better. Pick one idea and explain: - the target user and pain point; - how the feature is expected to change user behavior; - the north-star metric, primary success metric, and guardrail metrics; - tradeoffs across rider experience, driver experience, safety, reliability, and revenue; - how you would test the feature, including the randomization unit, experiment duration, power or MDE considerations, and how you would handle marketplace interference or spillovers. Part B: Safety trend analysis Assume the monthly accident rate for one city is defined as accidents per 100,000 completed trips. A line chart shows that the accident rate increases sharply from June through November and then drops quickly after November. Describe how you would analyze this pattern. Be explicit about: - validating the metric definition and its numerator and denominator; - plausible hypotheses for the increase and subsequent decline; - what internal data cuts and external data you would investigate; - how to distinguish a true safety change from a reporting or measurement artifact; - what statistical or causal methods you would use and what follow-up actions you would recommend.

Quick Answer: This question evaluates a data scientist's product-sense and analytical competencies, including feature ideation, metrics definition and validation, randomized experiment design with marketplace interference considerations, trade-off analysis across rider/driver/safety/revenue dimensions, and time-series and causal inference methods for safety trends in a ride-sharing marketplace. Commonly asked in analytics & experimentation interviews, it assesses both conceptual understanding and practical application by testing metric literacy, hypothesis generation, experiment power and randomization choices, and the ability to distinguish true safety changes from reporting or measurement artifacts.

You are interviewing for a Data Scientist summer internship at a ride-sharing marketplace.

Part A: Product case Uber wants ideas for a new rider-facing or driver-facing feature, or a major improvement to an existing workflow, that would make the app meaningfully better. Pick one idea and explain:

the target user and pain point;
how the feature is expected to change user behavior;
the north-star metric, primary success metric, and guardrail metrics;
tradeoffs across rider experience, driver experience, safety, reliability, and revenue;
how you would test the feature, including the randomization unit, experiment duration, power or MDE considerations, and how you would handle marketplace interference or spillovers.

Part B: Safety trend analysis Assume the monthly accident rate for one city is defined as accidents per 100,000 completed trips. A line chart shows that the accident rate increases sharply from June through November and then drops quickly after November. Describe how you would analyze this pattern. Be explicit about:

validating the metric definition and its numerator and denominator;
plausible hypotheses for the increase and subsequent decline;
what internal data cuts and external data you would investigate;
how to distinguish a true safety change from a reporting or measurement artifact;
what statistical or causal methods you would use and what follow-up actions you would recommend.

Design an Uber feature and analyze safety

Quick Overview

Solution

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