Lyft·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Lyft data science interview with a meaty product analytics case about diagnosing a driver satisfaction score drop. One question, but it had a lot of layers to it and I felt like I only partially got through all of them.

Questions Asked (1)

Lyft's driver satisfaction (WOW) score dropped 10% quarter-over-quarter. How would you investigate the root cause, what data and segments would you look at, and how would you design an experiment or product change to improve it? How do you measure success?

Product Analytics & MetricsRoot Cause AnalysisA/B Testing & Experimentation

Author's notes

I started with metric definition which was the right call, but I spent too long there and kind of rambled.

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AI HintsAI Generated

Suggested Approach

Start by validating the metric and decomposing the 10% drop across driver segments, geographies, and time to isolate where the decline is concentrated. Then form hypotheses about root causes (e.g., policy changes, competitor actions, product bugs) and prioritize them using data. Finally, design an experiment or product change to address the top driver, define success metrics (e.g., WOW score, retention), and outline how you'd measure impact.

Pro tip: Don't just focus on the average—drill down into driver cohorts (e.g., new vs. experienced, full-time vs. part-time) and consider external factors like seasonality or competitor promotions. Also, ensure you distinguish between correlation and causation by using control groups or quasi-experimental methods when a full A/B test isn't feasible.

1. Validate and decompose the metric

Confirm the drop is real (not a data pipeline issue) and break down the WOW score by dimensions like region, driver tenure, vehicle type, and time period to identify where the decline is largest.

2. Generate and prioritize hypotheses

Brainstorm potential root causes (e.g., changes in incentives, app updates, competitor entry, macroeconomic factors) and use data to assess which are most likely, such as checking for correlated changes in other metrics.

3. Deep dive into segments and drivers

Analyze the most affected segments to understand their specific pain points—e.g., survey drivers, review support tickets, and examine behavioral data (acceptance rates, earnings per hour) to pinpoint drivers of dissatisfaction.

4. Design an experiment or product change

Based on the root cause, propose a targeted intervention (e.g., new incentive structure, app feature) and design an A/B test with a control group, ensuring proper randomization and sample size.

5. Define success metrics and measure impact

Choose primary metrics (e.g., WOW score, driver retention) and guardrail metrics (e.g., ride completion rate, cost per ride). Plan to measure the experiment's effect over a sufficient duration and consider long-term effects.

Key Points to Mention

Segment the data by driver tenure, region, and engagement level to find the most affected groups.
Check for external factors like competitor launches, regulatory changes, or seasonality that could explain the drop.
Use driver feedback (surveys, support tickets) to complement quantitative analysis.
When designing an experiment, ensure it's randomized and has enough power to detect a meaningful change in WOW score.
Define both primary success metrics (e.g., WOW score improvement) and guardrail metrics (e.g., ride volume, driver earnings) to avoid unintended consequences.
Consider the trade-offs between short-term fixes and long-term structural changes, and how to measure their respective impacts.

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