##### Scenario Airport pickups: comparing a new model that predicts walk-time from order location to pickup zone against the current ETA model. ##### Question What metrics would you use to evaluate the new airport ETA model’s impact on rider and driver experience? Design an experiment to test the new model against the baseline. Riders may speed up or slow down based on displayed driver arrival time; how would you quantify this behavioral feedback loop and separate it from model accuracy? ##### Hints Consider rider wait, driver idle, cancellations; use staged rollout, delayed predictions, or instrumental variables to isolate behavioral response.

This question evaluates experimental design, metric selection, behavioral analytics, and causal inference skills for measuring a new ETA model’s impact on rider and driver experience within the Analytics & Experimentation domain.

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 hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Uber.

What role is this question designed for?

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

Evaluate New Model's Impact on Rider and Driver Experience

Airport Pickups ETA Model: Evaluation and Experiment Design

Context

A new model predicts rider walk-time from the order location (e.g., terminal/door) to the pickup zone. The current baseline ETA shown to riders reflects only the driver’s arrival. The product will display a combined pickup ETA (driver ETA ± walk time), which can change rider behavior: riders may speed up or slow down based on the displayed time.

Assumptions added for clarity:

You can log rider device location pings to infer arrival at pickup zones and approximate walking start/arrival times.
You can log driver arrival/departure times at pickup zones.
Both baseline and new model predictions can be logged, regardless of which is shown to the rider.

Tasks

Metrics: What would you use to evaluate the new airport ETA model’s impact on rider and driver experience?
Experiment: Design an experiment to test the new model against the baseline.
Feedback loop: Riders may adjust their pace based on displayed ETA. How would you quantify this behavioral response and separate it from pure model accuracy?

Context

Assumptions added for clarity:

You can log rider device location pings to infer arrival at pickup zones and approximate walking start/arrival times.

You can log driver arrival/departure times at pickup zones.

Both baseline and new model predictions can be logged, regardless of which is shown to the rider.

Tasks

Metrics: What would you use to evaluate the new airport ETA model’s impact on rider and driver experience?

Experiment: Design an experiment to test the new model against the baseline.

Feedback loop: Riders may adjust their pace based on displayed ETA. How would you quantify this behavioral response and separate it from pure model accuracy?

Evaluate New Model's Impact on Rider and Driver Experience

Quick Overview

Airport Pickups ETA Model: Evaluation and Experiment Design

Context

Tasks

Solution

Submit Your Answer to Earn 20XP

Evaluate New Model's Impact on Rider and Driver Experience

Quick Overview

Airport Pickups ETA Model: Evaluation and Experiment Design

Context

Tasks

Solution

Submit Your Answer to Earn 20XP