This is where I spent most of my time and also where I stumbled first.
Start by clarifying the feature's goal and the long-term value it aims to create, then propose a primary metric that captures that value (e.g., repeat bookings or host retention). Walk through the metric's formula with precise numerator/denominator definitions, deduplication, windowing, and outlier/bot handling, emphasizing how each choice aligns with long-term impact.
Pro tip: Tie the metric to a north-star business outcome and explicitly discuss trade-offs (e.g., short-term engagement vs. long-term retention) to show strategic thinking. Also, mention that you would validate the metric through A/B testing and monitor for unintended consequences.
Ask clarifying questions to understand the feature's purpose and the long-term value it should drive (e.g., increased trust, repeat usage). Align the metric with a strategic business goal.
Propose a metric that directly measures the long-term value, such as 'repeat booking rate within 90 days' or 'host retention rate after 6 months'. Explain why it's the best proxy for long-term success.
Detail the numerator and denominator: e.g., numerator = number of unique users who made a repeat booking within the window; denominator = number of unique users exposed to the feature. Define deduplication logic (e.g., count each user once) and windowing choices (e.g., 90-day window from first exposure).
Describe how to identify and exclude bot traffic (e.g., using user-agent, behavior patterns) and handle outliers (e.g., winsorizing, capping extreme values, or using robust statistics). Explain the impact on the metric.
Mention that you would validate the metric via A/B testing, check for guardrail metrics, and iterate if the metric doesn't correlate with long-term value. Discuss potential pitfalls and how to address them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the late events angle.
Start by defining both metrics and their typical use cases, then discuss how each handles late or backfilled events in terms of sensitivity and bias. Use a concrete example (e.g., click-through rate) to illustrate the trade-offs, and conclude with a recommendation based on the specific context and goals.
Pro tip: Mention that ratio-of-sums is generally more robust to late events because it aggregates totals before taking the ratio, while sum-of-ratios can be more sensitive to outliers and late data, but may be preferred when you need to weight each unit equally. Also, highlight the importance of aligning the metric with the randomization unit and the analysis unit.
Clearly define ratio-of-sums (e.g., total clicks / total views) and sum-of-ratios (e.g., average of per-user click-through rates). Explain that the choice depends on the unit of analysis and the goal of the experiment.
Compare the two approaches in terms of interpretability, statistical power, and sensitivity to outliers. Ratio-of-sums is typically more stable and easier to interpret, while sum-of-ratios can be dominated by users with few events.
Explain that late events affect both numerator and denominator, but ratio-of-sums may be less sensitive because it aggregates across all users. Sum-of-ratios can be more sensitive if late events disproportionately affect users with extreme ratios.
Use a scenario like measuring click-through rate: if a user clicks late, their ratio changes, but in ratio-of-sums the overall ratio may not shift much if many users are included. In sum-of-ratios, that user's ratio could have a large impact if they have few views.
Conclude that the choice depends on the metric's purpose, the randomization unit, and the expected delay patterns. Suggest using ratio-of-sums for global metrics and sum-of-ratios when per-unit effects are of interest, and always monitor for late data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining how you would detect a silent logging change through automated data quality checks and anomaly detection on log volumes and metric distributions. Then describe a correction strategy that involves isolating the affected period, adjusting metrics using pre-change baselines, and validating with a holdout. Finally, discuss how timezone misalignment can shift daily boundaries, causing misattribution of events and biased experiment results.
Pro tip: Emphasize the importance of logging schema versioning and timezone-aware timestamps in experiment design to prevent such issues. Mention that at Airbnb, where users span multiple timezones, aligning on UTC for analysis but local time for user experience is critical.
Implement automated data quality checks (e.g., row counts, null rates, distribution shifts) and anomaly detection to flag sudden changes in logging behavior. Monitor log volume and key metric trends in real-time.
Investigate the root cause by comparing log schemas, timestamps, and event definitions before and after the change. Check deployment logs and code changes to identify when and why the logging change occurred.
Isolate the affected period and adjust metrics by re-weighting or using pre-change baselines. If possible, re-run the experiment or use a holdout group to validate corrections.
Explain how timezone misalignment shifts daily boundaries, causing events to be assigned to wrong days, leading to biased daily metrics and incorrect experiment conclusions. Use UTC for analysis and ensure all timestamps are timezone-aware.
Advocate for logging schema versioning, timezone standardization, and automated monitoring to prevent future silent changes. Include data quality checks in the experiment pipeline.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Power calculations I can do in my sleep so this part was fine.
Start by clarifying assumptions and defining the metric, then walk through the MDE and sample size calculation using historical variance, power, and significance level. Finally, discuss sequential monitoring with alpha spending functions to control false positives over the 14-day window.
Pro tip: Emphasize that sequential monitoring requires pre-registration of the spending function and that peeking without adjustment inflates Type I error; mention Airbnb's likely use of tools like sequential testing or group sequential designs.
Confirm the metric (e.g., conversion rate), baseline variance from historical experiments, desired power (typically 80%), significance level (5%), and whether the test is one- or two-sided.
Use the formula for MDE given sample size or solve for sample size given MDE, incorporating historical variance. For a 14-day test, consider daily traffic to ensure feasibility.
Describe how to monitor results daily while controlling Type I error. Introduce alpha spending functions (e.g., O'Brien-Fleming, Pocock) that allocate alpha over the 14 days.
Address trade-offs between early stopping and power, the impact of multiple looks, and how to choose a spending function based on business needs (e.g., early detection vs. strict error control).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Picked crash rate, p95 latency, and complaint rate.
Start by defining three guardrail metrics that cover different risk areas (e.g., user experience, business health, system performance) and set canary thresholds based on historical variability and practical significance. Then, outline a decision framework for when the primary metric improves but a guardrail regresses slightly, emphasizing investigation, risk assessment, and potential mitigation before deciding to ship or iterate.
Pro tip: Frame guardrails as 'canary metrics' that act as early warning signals—set thresholds at a level that balances sensitivity and false positives, and always consider the cost of a false negative (shipping a harmful change) versus a false positive (delaying a beneficial change).
Choose three metrics that capture potential negative impacts: e.g., user engagement (e.g., booking completion rate), business health (e.g., revenue per user), and system performance (e.g., page load time). Ensure they are sensitive to changes and aligned with company goals.
For each guardrail, define a threshold (e.g., -2% relative change) based on historical variance, minimum detectable effect, and business tolerance. Use statistical significance and practical significance to avoid overreacting to noise.
During the experiment, continuously monitor guardrails. If a guardrail shows a slight regression (e.g., -1.5% when threshold is -2%), flag it for investigation but do not automatically stop the experiment.
If primary metric improves but a guardrail regresses slightly, dig into segment-level data, check for novelty effects, and quantify the net impact. Consider whether the regression is acceptable given the primary gain.
Based on investigation, either ship with monitoring, iterate to mitigate the guardrail regression, or abandon if the risk outweighs benefits. Document the decision and learnings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.