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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 Lyft.

What role is this question designed for?

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

Investigate Causes and Effects of Dynamic Pricing on ETAs

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in diagnostic analytics, demand–supply and pricing analysis, causal inference, cost–benefit assessment, and experimental design within the Analytics & Experimentation domain.

Lyft

Jul 12, 2025, 6:59 PM

Data Scientist

Technical Screen

Analytics & Experimentation

Lyft ETA Increase, Pricing Strategy, and Experiment Design

Context

Lyft observes a 20% month-over-month increase in rider wait times (ETA). Assume ETA refers to the time from ride request to driver arrival, reported by median (p50) unless otherwise noted.
Pricing has been static (fixed base + time/distance rates with no surge). The team suspects static pricing may be causing demand–supply imbalances.
Goal: Diagnose the root causes, assess the costs/benefits of moving to dynamic pricing, and design an experiment to test whether dynamic pricing reduces ETAs without harming other key metrics.

Tasks

Diagnose the 20% MoM ETA increase systematically.
Assess the costs and benefits of switching from static to dynamic pricing.
Design an experiment to test whether dynamic pricing reduces ETAs without negatively impacting other KPIs.

Hints

Consider rider/driver funnels, cohort analyses, geo/time splits, demand–supply interference, metric selection, and experiment-design choices.

Solution

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