This is where I spent the most time and probably still left gaps.
Start by clarifying the notification's purpose and the user action it aims to drive, then define a North Star metric that captures the core value. Structure your answer by proposing one North Star, three driver metrics that directly influence it, and three guardrail metrics that ensure no harm, specifying exact formulas, denominators, and attribution windows. Finally, classify each metric as leading or lagging to show strategic thinking.
Pro tip: Tie your metrics to Meta's emphasis on meaningful social interactions and long-term user value; avoid vanity metrics like raw notification opens and instead focus on downstream engagement like event attendance or re-engagement with the friend.
Define the notification's objective (e.g., drive event attendance or strengthen social ties) and map the user journey from receiving the notification to taking action. This ensures metrics align with business and user value.
Choose a single metric that best captures the notification's success, such as the rate of users who attend the event after receiving the notification. Provide an exact formula, denominator, and attribution window.
Select three metrics that directly influence the North Star, such as notification open rate, event page visit rate, and RSVP rate. For each, specify formula, denominator, and attribution window.
Choose three metrics to monitor for negative side effects, such as notification dismissal rate, user report rate, and notification opt-out rate. Define each with formula, denominator, and attribution window.
Explain which metrics are leading (predictive of future success, e.g., open rate) and which are lagging (outcome-based, e.g., event attendance). This shows understanding of metric timing and causality.
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Four biases came to me pretty quickly: event-type confounding (concerts vs.
First, identify and explain the biases in the naive estimator, such as selection bias, survivorship bias, and confounding, and propose corrections like stratification, weighting, or causal inference methods. Then, for the confidence interval, outline the appropriate statistical method (e.g., normal approximation or bootstrap) and the necessary assumptions, ensuring to mention data requirements and potential pitfalls.
Pro tip: Show that you understand the business context by linking each bias to a specific product decision and quantifying the potential impact of the correction. Also, when computing the confidence interval, clarify whether you're treating daily opens as independent or if there's temporal correlation, and adjust accordingly.
List at least four biases: selection bias (non-random event participation), survivorship bias (only successful events included), confounding (event size or seasonality), and measurement bias (inaccurate friend counts). Explain how each skews the estimate.
For each bias, suggest a correction: e.g., use stratified sampling or inverse probability weighting for selection bias; include all events or model event success for survivorship; control for confounders via regression or matching; validate friend data for measurement bias.
Define the metric: average number of notification opens per day per user. Decide on the data source and time window, and consider whether to model at user-level or aggregate level.
Choose a method: if opens are approximately normal, use mean ± 1.96*SE; if skewed or count data, use Poisson or negative binomial model; if temporal correlation, use time series or bootstrap. State assumptions and show calculation.
Check assumptions (e.g., normality, independence) and consider sensitivity analysis. Communicate the CI in business terms, noting that it reflects uncertainty in the estimate.
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I sketched out a rough formula: incremental retention lift times LTV per retained user, summed over the expected user base, has to exceed dev cost plus infra cost over some payback period.
Start by framing the feature's value in terms of incremental business metrics (opens, sessions, retention) and then translate those into a break-even condition where expected incremental revenue equals engineering and infrastructure costs. Derive a formula that solves for the minimum detectable effect (MDE) required to justify the investment, and discuss how to measure it with an experiment.
Pro tip: Anchor your answer in the company's north-star metric and show that you understand the trade-off between statistical power and development cost—this demonstrates product sense and analytical rigor.
Express the feature's expected impact as incremental opens, sessions, and retention, and map these to a monetary value (e.g., via ad revenue per session or LTV).
List all costs: dev-weeks (converted to dollars using fully-loaded engineer cost) and ongoing infrastructure cost (e.g., cloud, storage, compute).
Set total incremental value equal to total cost and solve for the required effect size. For example: (Δopens * V_open + Δsessions * V_session + Δretention * V_retention) * N = C_dev + C_infra.
Rearrange the formula to isolate the MDE (e.g., Δsessions) given baseline metrics, costs, and expected user base. This MDE is the threshold the experiment must detect to justify proceeding.
Check if the MDE is achievable given traffic and variance; if not, recommend not proceeding or re-scoping the feature.
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I talked about using survey intent data discounted by a response bias correction factor, basically applying a historical ratio of stated-to-actual behavior from prior similar features.
Start by outlining a structured pre-launch evidence plan that combines concept tests, surveys, and analog benchmarks to estimate CTR uplift. Then, discuss how to adjust for response bias and population mismatch using statistical techniques and validation steps. Emphasize the iterative nature of refining predictions as more data becomes available.
Pro tip: Leverage Meta's internal tools and historical data to calibrate analog benchmarks, and always validate predictions with a small-scale A/B test before full launch to mitigate risks.
Outline the components: concept tests to gauge initial appeal, surveys to measure intent, and analog benchmarks from similar past launches to predict CTR uplift.
Execute concept tests and surveys, ensuring representative sampling. Gather analog benchmarks from historical data, adjusting for differences in context.
Identify potential response bias (e.g., social desirability) and population mismatch (e.g., test group vs. target). Apply weighting, stratification, or modeling techniques to correct.
Combine adjusted data to predict CTR uplift. Validate predictions with a holdout or small-scale A/B test to assess accuracy and refine the model.
Refine the plan based on validation results. Communicate assumptions, limitations, and confidence intervals to stakeholders.
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Network effects question at Meta, predictable but still tricky to get right.
Start by clarifying the notification feature and its potential for network effects, then systematically define the randomization unit, metrics, sample size, and interference detection plan. Emphasize the trade-offs between user-level and cluster randomization, and outline a robust process for detecting and mitigating contamination.
Pro tip: Always consider the social graph at Meta: even if the feature seems individual, interactions like notifications can spill over. Proactively discuss cluster randomization and interference detection to show depth.
Understand the notification feature's mechanics and how it might create interference between users (e.g., sharing, social pressure). Determine if network effects are likely.
Decide between user-level and cluster randomization based on interference risk. If interference is expected, use cluster randomization (e.g., by social community or geographic region).
Select primary metric (e.g., notification engagement) and guardrail metrics (e.g., user retention, notification fatigue). Calculate sample size using power analysis, accounting for cluster design if applicable.
Plan methods to detect interference: compare outcomes between treated and control clusters, use social network analysis, or run A/A tests with cluster randomization. Monitor for spillover effects.
If contamination is found, consider re-randomizing, using cluster-based analysis, or adjusting the experiment design (e.g., switch to cluster randomization). Document and learn from the issue.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the experiment's goal and defining a clear metric hierarchy with a primary success metric (e.g., long-term user engagement) and guardrail metrics (unsubscribe rate, notification volume). Then, evaluate the trade-offs using a utility function that weights the primary metric against guardrails, and make a go/no-go decision based on whether the net utility is positive and guardrails are not violated.
Pro tip: Emphasize that short-term CTR decreases might be acceptable if long-term engagement increases, but always check for novelty effects and ensure guardrails are not breached. Consider segment-level analysis to see if the trade-off varies across user groups.
Identify the primary objective (e.g., increase long-term user engagement) and establish a hierarchy: primary metric (time-on-site), secondary metrics (CTR), and guardrail metrics (unsubscribe rate, notification volume).
Check if unsubscribe rate and notification volume are within acceptable thresholds. If guardrails are violated, the experiment may be a no-go regardless of primary metric improvement.
Define a utility function that combines the primary metric and secondary metrics, with weights reflecting business priorities. For example, utility = w1 * Δtime-on-site - w2 * ΔCTR - w3 * Δunsubscribe rate.
Ensure the observed changes are statistically significant and consider the effect sizes. A small but significant increase in time-on-site might not outweigh a large decrease in CTR.
If net utility is positive and guardrails are satisfied, recommend a go; otherwise, no-go. Suggest further analysis (e.g., segment-level, long-term holdout) if ambiguous.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.