I started with the obvious, proportion of new posts getting a non-deleted comment within 24 hours, but then realized I was glossing over what 'meaningful' actually means operationally.
Start by clarifying the goal and defining what constitutes a 'meaningful comment' in the context of Meta's products. Then, outline a measurement framework that includes a primary success metric, guardrail metrics, and a plan for experimentation to validate improvements.
Pro tip: Emphasize the importance of aligning the metric with long-term user value and business goals, and consider potential trade-offs such as increased moderation costs or decreased overall engagement.
Ensure alignment on what 'meaningful' means—e.g., comments with a minimum length, sentiment, or replies—and consider product-specific nuances. This definition will drive the metric.
Select a metric that directly measures the goal, such as the percentage of posts with at least one meaningful comment, or the average number of meaningful comments per post. Consider using a ratio to account for post volume.
Monitor metrics like overall engagement, user retention, comment quality, and moderation reports to ensure improvements don't harm other areas. Also track secondary metrics like time to first comment.
Propose an A/B test where the treatment aims to increase meaningful comments (e.g., via ranking changes or prompts). Define success criteria, sample size, and duration, and analyze results with statistical rigor.
If the experiment shows a significant positive impact without harming guardrails, recommend scaling. If not, analyze why and iterate on the approach.
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Used 28 days of data as the window, which felt right.
Start by clarifying the metric and its definition, then outline a data-driven process to compute baseline statistics and MDE using recent historical data. Emphasize the importance of accounting for seasonality and other time-based patterns, and propose methods to detect and adjust for them.
Pro tip: When estimating MDE, don't just rely on formulas—simulate the experiment using historical data to account for real-world complexities like variance and seasonality. Also, consider practical significance, not just statistical significance, to align with business goals.
Define the metric precisely, including its calculation, aggregation level, and any known nuances. Confirm the experiment design (e.g., unit of randomization, duration) and business objectives.
Use recent historical data (e.g., last 4-8 weeks) to compute the metric's average, variance, and distribution. Segment by relevant dimensions (e.g., device, region) to understand heterogeneity.
Calculate MDE using power analysis (e.g., 80% power, 5% significance) based on baseline variance and sample size. Alternatively, simulate experiments by resampling historical data to derive MDE empirically.
Analyze time series patterns (e.g., day-of-week, holidays, trends) using decomposition or autocorrelation. Flag any periods that could confound results and adjust baseline/MDE accordingly.
Sanity-check assumptions, document limitations, and propose mitigation (e.g., longer test, stratification). Communicate findings to stakeholders, highlighting risks and recommendations.
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Structure your answer around a clear framework that covers the four areas mentioned, ensuring you generate at least 10 ideas. For each idea, briefly state the mechanism, expected effect size, and risks. Conclude by prioritizing ideas based on impact and feasibility.
Pro tip: Quantify effect sizes using ranges (e.g., '5-10% lift') and ground them in analogous features or A/B tests you know. Acknowledge trade-offs like spam or reduced content quality, showing you understand the platform's complexity.
Define what 'comment rate' means (e.g., comments per post view or per user) and confirm the objective is to increase meaningful comments, not just quantity. Mention guardrail metrics like user satisfaction and spam rates.
Brainstorm at least 10 ideas, ensuring coverage of: making commenting easier, increasing intent, better matching posts to likely commenters, and notification/feed changes. Aim for 2-3 ideas per category.
For each idea, provide a rough expected effect size (e.g., low/medium/high or percentage lift) and identify potential risks such as spam, reduced content quality, or user annoyance. Use analogies or past experiments to justify estimates.
Rank the ideas based on expected impact, implementation cost, and risk. Recommend a few to test first, explaining your reasoning. Suggest how to measure success via A/B tests.
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Pretty straightforward to rattle off: post and comment creation timestamps, user network graph, content type, notification send and open events, dwell time.
Start by clarifying the feature's goal and the key user behaviors it aims to influence, then map those to measurable metrics and the data needed to compute them. Structure your answer around a metrics framework (e.g., HEART or AARRR) and describe how you'd collect data for both success metrics and guardrail metrics, ensuring you can run valid experiments.
Pro tip: Emphasize the importance of instrumenting data at the user level with unique identifiers and timestamps to enable cohort analysis and causal inference, and mention how you'd handle data quality and privacy considerations (e.g., GDPR) early on.
Ask questions to understand what the feature is, its intended impact, and the hypotheses you want to test. This ensures data collection aligns with business objectives.
Identify primary success metrics (e.g., engagement, conversion) and guardrail metrics (e.g., latency, user churn) that will indicate whether the feature is working as intended without negative side effects.
For each metric, specify the raw data needed: event logs, user attributes, session data, etc. Consider both online (real-time) and offline (batch) data sources.
Outline how data will be captured: event tracking, logging, surveys, or external data. Ensure data is reliable, timely, and includes necessary dimensions (user ID, timestamp, experiment group).
Describe how the data will support A/B testing, causal inference, and deep dives. Mention the need for control groups, randomization, and sufficient sample size.
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Network interference tripped me up more than I expected.
Start by briefly stating your two ideas and why they are top priorities, then for each idea walk through the experimental design choices in a structured way. Emphasize how you would detect and mitigate network interference, justify your power assumptions, and outline a ramp plan with guardrails and spillover diagnostics.
Pro tip: Meta cares deeply about social network effects, so show you understand that standard A/B tests can be biased by interference. Mention concrete techniques like cluster randomization, graph clustering, or switchback designs, and tie them to the specific product context.
Briefly describe the two ideas and justify their prioritization based on potential impact and strategic alignment. This sets the stage for the experimental design.
Cover unit of randomization (e.g., user, cluster, time), how you handle network interference (e.g., cluster randomization, ego network isolation), ramp plan (e.g., 1% -> 5% -> 50%), power assumptions (baseline metric, MDE, alpha, power, sample size), guardrails (e.g., user engagement, revenue, latency), and spillover diagnostics (e.g., measure cross-group interactions, compare to historical).
Repeat the same structured design for the second idea, highlighting any differences in randomization, interference handling, ramp, power, guardrails, and diagnostics.
Discuss trade-offs between the two designs, such as complexity, risk, and speed. Explain how you would choose which to run first or whether to run both.
Recap key decisions and emphasize how you would monitor and adapt the experiments based on early results and diagnostics.
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Attribution was interesting to think through.
Start by defining the metric and the experiment design, then outline a step-by-step analysis plan that covers attribution, behavioral distinction, and guardrail monitoring. Emphasize causal inference techniques and the importance of aligning with product goals for the ship decision.
Pro tip: Always consider the 'why' behind the metrics—distinguishing between new and shifted behavior is crucial for understanding long-term impact. Also, proactively check for novelty effects and ensure your analysis accounts for multiple comparisons.
Clarify the primary metric (e.g., comments per user) and guardrail metrics (e.g., abuse reports, quality scores). Ensure the experiment has proper randomization, sufficient power, and a predefined analysis plan.
Use cohort analysis to separate new commenters from existing ones. Apply techniques like difference-in-differences or user-level attribution to isolate incremental lift from shifted behavior.
Track guardrail metrics such as report rate, spam detection, and sentiment analysis. Segment by user type to detect disproportionate impacts and ensure no degradation in comment quality.
Weigh the primary metric lift against guardrail violations and long-term impact. Consider statistical significance, practical significance, and potential novelty effects before deciding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.