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Design robust primary and guardrail metrics

Last updated: Mar 29, 2026

Quick Overview

This question evaluates proficiency in experiment metric design, guardrail definition, data quality diagnostics, and statistical analysis for A/B testing, including power calculations and sequential monitoring, within the Analytics & Experimentation domain.

  • hard
  • Airbnb
  • Analytics & Experimentation
  • Data Scientist

Design robust primary and guardrail metrics

Company: Airbnb

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

For a newly launched feature, define a primary metric that best maps to long-term value and at least three guardrails. Specify exact formulas, units, numerator/denominator definitions, event deduplication rules, windowing (e.g., 7-day vs same-day), and handling of outliers (e.g., winsorization) and bots. Discuss ratio-of-sums vs sum-of-ratios trade-offs, sensitivity to backfilled/late events, timezone alignment, and how you would detect and correct a silent logging change mid-experiment. Compute power and MDE for a 14-day test given historical variance; explain sequential monitoring strategy and alpha spending. Propose canary thresholds for guardrails (e.g., crash rate, p95 latency, complaint rate), plus what to do when the primary improves but a guardrail slightly regresses.

Quick Answer: This question evaluates proficiency in experiment metric design, guardrail definition, data quality diagnostics, and statistical analysis for A/B testing, including power calculations and sequential monitoring, within the Analytics & Experimentation domain.

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Airbnb logo
Airbnb
Oct 13, 2025, 9:49 PM
Data Scientist
Technical Screen
Analytics & Experimentation
5
0

Experiment Metric Design, Guardrails, and Power for a 14-Day A/B Test

Context

You are testing a newly launched, guest-facing booking feature in a global, two-sided travel marketplace. Randomization occurs at the user level. Users become "exposed" when they first encounter an eligible surface during a 14-day enrollment window. You want a primary metric that maps to long-term value and a set of guardrails to protect user experience and platform health.

Tasks

  1. Define a single primary metric that best maps to long-term value. For this metric, specify:
    • Exact formula, units, numerator/denominator definitions
    • Event deduplication rules
    • Windowing (e.g., enrollment window vs. attribution window)
    • Handling of outliers (e.g., winsorization) and bots/fraud filters
    • Currency normalization and timezone alignment
    • Choice and justification of ratio-of-sums vs. sum-of-ratios
    • Sensitivity to late/backfilled events
  2. Define at least three guardrail metrics with:
    • Exact formulas, windows, units, dedup rules (if applicable)
    • Outlier handling and bot filtering
  3. Describe how you would detect and correct a silent logging change mid-experiment.
  4. Compute statistical power and MDE for a 14-day test given historical variance; show the formula and a worked example with reasonable assumptions.
  5. Explain a sequential monitoring strategy and alpha spending approach for interim looks.
  6. Propose canary thresholds for guardrails (e.g., crash rate, p95 latency, complaint rate) and describe what you would do if the primary improves but a guardrail slightly regresses.

Solution

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