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 medium 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 Rideshare Marketplace Causal Analyses

Quick Overview

This question evaluates a data scientist's skills in experimental design and causal inference, marketplace metrics and diagnostics, fairness and safety impact assessment, incentive and policy evaluation, and ETA modeling and forecast quality measurement.

You are a data scientist at a ride-hailing marketplace. Answer the following case prompts as if you were advising product, operations, and marketplace leadership.

High-risk drop-off warning for drivers : The company wants to introduce an in-app prompt that tells drivers whether a passenger's drop-off area is considered a high-risk neighborhood. How would you evaluate this feature experimentally? What business, safety, marketplace, and fairness impacts would you measure? If the company can run the test across many cities, how would that change your experiment design?
ETA importance and improvement : Explain why estimated time of arrival, or ETA, is important in a ride-hailing product. What metrics would you use to measure ETA quality? What product, data, or machine-learning approaches would you consider to diagnose and improve ETA accuracy?
Advance driver promotion for specific times and places : The company wants to notify drivers one week in advance that they can earn a promotion if they go online and accept trips in specific locations during specific time windows. What effects could this incentive have? Why might a simple user-level A/B test be inappropriate? What problems would arise with a switchback experiment? If you instead used a synthetic control approach, how would you estimate uncertainty or variance?

Quick Overview

You are a data scientist at a ride-hailing marketplace. Answer the following case prompts as if you were advising product, operations, and marketplace leadership.

High-risk drop-off warning for drivers : The company wants to introduce an in-app prompt that tells drivers whether a passenger's drop-off area is considered a high-risk neighborhood. How would you evaluate this feature experimentally? What business, safety, marketplace, and fairness impacts would you measure? If the company can run the test across many cities, how would that change your experiment design?
ETA importance and improvement : Explain why estimated time of arrival, or ETA, is important in a ride-hailing product. What metrics would you use to measure ETA quality? What product, data, or machine-learning approaches would you consider to diagnose and improve ETA accuracy?
Advance driver promotion for specific times and places : The company wants to notify drivers one week in advance that they can earn a promotion if they go online and accept trips in specific locations during specific time windows. What effects could this incentive have? Why might a simple user-level A/B test be inappropriate? What problems would arise with a switchback experiment? If you instead used a synthetic control approach, how would you estimate uncertainty or variance?

Design Rideshare Marketplace Causal Analyses

Quick Overview

Solution

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Design Rideshare Marketplace Causal Analyses

Quick Overview

Solution

Comments (0)