Investigate Sudden Metric Changes and Design A/B Test
Quick Overview
Investigate Sudden Metric Changes and Design A/B Test evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Investigate Sudden Metric Changes and Design A/B Test
Company: Lyft
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
A key business metric suddenly spikes or drops; leadership asks for a root-cause analysis and later wants to run an A/B test on a proposed fix.
##### Question
How would you systematically investigate the cause of a sudden change in a core metric? 2. Outline the essential steps, assumptions, and success criteria for designing and analyzing an A/B experiment to validate a potential solution.
##### Hints
Think root-cause frameworks (segmentation, funnel, time), define control/treatment, randomization, power, sample size, success metric.
Quick Answer: Investigate Sudden Metric Changes and Design A/B Test evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Investigate Sudden Metric Changes and Design A/B Test
Scenario
A core business metric (e.g., conversion, cancellations, or gross bookings) shows a sudden spike or drop. Leadership asks for a rapid root-cause analysis (RCA), and then wants an A/B experiment to validate a proposed fix.
Assume you are the data scientist on a two-sided consumer marketplace with strong diurnal and day-of-week patterns. You have access to product analytics, experimentation logs, feature-flag rollouts, marketing spend, and operational metrics.
Tasks
Root-cause analysis: Describe a systematic approach to investigate the cause of the sudden change. Specify what you would check first (triage), how you would segment the problem, and how you would quantify contributions of different factors.
A/B testing plan: Outline the essential steps, assumptions, and success criteria for designing and analyzing an experiment to validate a potential solution. Include definition of control/treatment, randomization unit, sample size and power, duration, primary/secondary metrics, guardrails, and analysis/decision criteria.
Hints
Root-cause frameworks: segmentation (who/where), funnel (where in the journey), time (when), external factors; rate vs. mix decomposition; change-point detection.
Experiment design: define hypothesis, control/treatment, randomization strategy and unit, handling interference, power/MDE/sample size, duration, instrumentation, guardrails, analysis plan, and rollout criteria.
Constraints & Assumptions
Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask Guidance
Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.
What a Strong Answer Covers Guidance
A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions Guidance
What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?