I went with watch time pretty quickly and then had to defend it against retention.
Start by clarifying the goal of the recommendation system—likely maximizing long-term user engagement and satisfaction—then propose a single North Star metric that balances user value and business value, such as 'time spent per user per day' or 'retention rate'. Justify why this metric is superior to alternatives by linking it to the system's objective and acknowledging potential trade-offs.
Pro tip: Choose a metric that is both sensitive to changes in the recommendation algorithm and aligned with long-term user retention, not just short-term clicks. Mention that you would pair it with guardrail metrics to prevent optimizing for the wrong behavior.
Confirm that the recommendation system aims to maximize long-term user engagement and satisfaction, not just immediate clicks or views.
Select a metric like 'daily time spent per user' or '7-day retention rate' that captures sustained engagement and reflects the system's success.
Explain why this metric is better than alternatives (e.g., CTR, likes) by linking it to user value and business goals, and noting its sensitivity to algorithm changes.
Discuss potential downsides (e.g., optimizing for time spent might reduce content diversity) and suggest guardrail metrics like user satisfaction or report rate.
Briefly outline how you would measure the metric (e.g., A/B testing) and iterate, showing a data-driven mindset.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This tripped me up more than it should have.
Start by selecting a metric relevant to Meta's products, such as daily active users (DAU) or time spent per user, and explicitly state any assumptions about its distribution (e.g., right-skewed). Sketch the distribution on a whiteboard or paper, clearly labeling the median, mode, and 95th percentile, and explain how their relative positions reflect the distribution's shape and what that implies for product decisions.
Pro tip: Always connect the metric's distribution to actionable product insights—for example, a long right tail might indicate a small group of power users driving engagement, which could inform feature development or targeting strategies.
Select a metric that is meaningful for Meta's products, such as daily active users, time spent per user, or number of messages sent. Briefly justify why this metric matters for the business.
Describe the expected shape of the distribution (e.g., right-skewed, normal, bimodal) based on domain knowledge or typical user behavior. Mention any factors that could influence the shape.
Draw a rough curve on a whiteboard or paper, labeling the x-axis with the metric and the y-axis with frequency or density. Ensure the curve reflects the stated assumptions.
Mark the positions of the median, mode, and 95th percentile on the sketch. Explain their relative positions (e.g., mode < median < 95th percentile for right-skewed data) and what they indicate about the data.
Discuss what the distribution and the labeled points imply for product decisions, such as identifying power users, setting performance targets, or designing experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metrics and their relationship, then assess the statistical significance and practical impact of both changes. Evaluate the tradeoff using a holistic framework like the OEC or guardrail metrics, and recommend a decision based on the product's strategic priorities and long-term user value.
Pro tip: Show that you understand the difference between a true tradeoff and a temporary dip, and always consider the counterfactual—what would have happened without the change. Demonstrating that you can align stakeholders on the decision criteria is as important as the analysis itself.
Define the primary and secondary metrics, their expected relationship, and the experiment's goal. Confirm if the secondary metric is a guardrail or a driver of long-term value.
Check if the changes are statistically significant and not due to noise. Consider confidence intervals, p-values, and practical significance.
Quantify the size of the increase and decrease, and determine if the secondary metric drop is acceptable given the primary gain. Consider segment-level analysis to see if the tradeoff varies across users.
Use a holistic metric like the Overall Evaluation Criterion (OEC) or consider long-term proxies. Align with product strategy: does the primary metric align with long-term goals, and is the secondary metric a leading indicator of churn or dissatisfaction?
Decide whether to ship, iterate, or kill the change based on the tradeoff analysis. Communicate the decision and rationale clearly to stakeholders, and propose follow-up experiments if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered randomization unit, power calculation, runtime, guardrails, and ship criteria.
Structure your answer around the end-to-end experimentation lifecycle, from defining a clear hypothesis and success metrics to designing the test, analyzing results, and making a ship/no-ship decision. Emphasize statistical rigor, guardrail metrics, and how you'd handle practical challenges like network effects or novelty effects. Tailor your response to Meta's scale and culture by mentioning rapid iteration and cross-functional collaboration.
Pro tip: Show that you think beyond statistical significance by discussing practical significance and the business impact of the decision. Also, mention how you'd handle multiple testing corrections and segment-level analyses to uncover heterogeneous treatment effects.
Articulate a clear, testable hypothesis about how the new recommender feed will improve user engagement. Define primary success metrics (e.g., CTR, time spent) and guardrail metrics (e.g., user satisfaction, retention) to ensure no harm.
Choose randomization unit (e.g., user-level), determine sample size and power, set experiment duration, and decide on control/treatment variants. Consider stratification and whether to run a holdback or switchback design if network effects are a concern.
Launch the experiment, monitor data quality, and check for sample ratio mismatch (SRM). Track guardrail metrics in real-time to catch any negative impact early and pause if necessary.
Perform statistical tests (e.g., t-test, bootstrap) to measure treatment effects, adjust for multiple comparisons, and conduct subgroup analyses. Assess both statistical and practical significance.
Based on results, decide to ship, iterate, or abandon. Document learnings, and if shipping, plan for a gradual rollout with continued monitoring. If iterating, refine hypothesis and rerun.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.