This is where I spent the most mental energy.
Start by defining a weighted composite score that combines relevancy and accuracy, then justify the weights based on the relative cost of errors (false positives vs. false negatives). Finally, explain how you would calibrate the weights using business outcomes like user engagement or revenue, potentially through A/B testing or regression analysis.
Pro tip: Emphasize that the weights should be dynamic and context-dependent, and propose a method to continuously learn and adjust them based on real-time feedback, showing a product-minded and iterative approach.
Propose a weighted sum of relevancy and accuracy, e.g., Score = w1 * Relevancy + w2 * Accuracy, where w1 + w2 = 1. Discuss possible variations like harmonic mean if both are equally important.
Explain that weights reflect the relative cost of errors: if a false positive (irrelevant but accurate) is more costly, increase w1; if a false negative (relevant but inaccurate) is more costly, increase w2. Use examples to illustrate.
Describe how to use historical data or experiments to find weights that maximize a business KPI (e.g., click-through rate, conversion). Suggest methods like logistic regression, grid search, or multi-armed bandits.
Propose A/B testing to validate the chosen weights and monitor for shifts in user behavior or business goals. Emphasize the need for periodic recalibration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly tripped me up more than I expected for something that sounds procedural.
Start by clarifying the metric's purpose and the decision it informs, then recommend a primary aggregation level (e.g., user-day) while acknowledging trade-offs. Explain how to handle users with many queries via weighting or capping, missing labels via imputation or exclusion, and session correlation via clustering or mixed-effects models.
Pro tip: Emphasize that the choice of aggregation should align with the experimental unit and the business decision; for A/B tests, aggregating at the randomization unit (often user) avoids inflated false positives due to correlation.
Define what the metric measures and the decision it supports (e.g., product change, model evaluation). This determines the appropriate aggregation level.
Recommend aggregating at the user-day level as a balance between granularity and stability, but note that query-level may be needed for debugging and session-level for behavioral analysis.
Use weighting (e.g., inverse query count) or capping to prevent power users from dominating; alternatively, model at the user level with query count as a feature.
Assess missingness mechanism; if random, exclude or impute; if systematic, incorporate missingness as a feature or use multiple imputation.
Use cluster-robust standard errors, mixed-effects models, or aggregate to user level to account for within-session correlation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the search feature and its goals, then propose a randomized controlled experiment with a well-justified randomization unit (e.g., user-level). Define primary success metrics and guardrail metrics, outline power analysis inputs, and describe pre-registration and p-hacking prevention strategies. Emphasize the importance of pre-registering the analysis plan and using techniques like Bonferroni correction or false discovery rate control for multiple comparisons.
Pro tip: At Meta, where experimentation velocity is high, it's crucial to balance statistical rigor with practical constraints; consider using sequential testing or variance reduction techniques like CUPED to detect smaller effects faster without inflating false positives.
Ask clarifying questions about the search feature (e.g., ranking algorithm change, UI update) and articulate the null and alternative hypotheses for the experiment.
Select the randomization unit (e.g., user, session, query) based on the feature and network effects. Define primary metrics (e.g., CTR, success rate) and guardrail metrics (e.g., latency, user satisfaction).
Specify inputs: baseline metric values, minimum detectable effect (MDE), significance level (α), power (1-β), and variance. Use these to compute required sample size per variant.
Document the analysis plan before launching: primary and secondary metrics, statistical tests, handling of multiple comparisons, and any subgroup analyses. This prevents p-hacking and ensures transparency.
For many slices, use corrections like Bonferroni or Benjamini-Hochberg, or pre-specify a limited set of slices. Consider hierarchical models or holdout validation to avoid false positives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Simpson's paradox question at the end caught me a bit off guard because I wasn't expecting it to get that specific.
Structure your answer around the full lifecycle of an offline evaluation pipeline: label collection, agreement measurement, golden set curation, and continuous monitoring. Emphasize how you would detect and address label drift and Simpson's paradox when slicing by query intent or locale, using statistical and visualization techniques. Tie everything back to ensuring reliable model evaluation and business impact.
Pro tip: Proactively mention that Simpson's paradox often arises when segment sizes shift over time, so you should monitor both segment-level and overall metrics, and use weighted averages or hierarchical models to reconcile them. Also, highlight the importance of documenting label guidelines and versioning golden sets to prevent silent regressions.
Define clear labeling guidelines, choose annotators (internal or crowdsourced), and implement quality checks like gold questions and consensus rounds. Use stratified sampling to ensure coverage across query intents and locales.
Compute agreement metrics (e.g., Cohen's kappa, Fleiss' kappa, Krippendorff's alpha) and set thresholds for acceptable reliability. Establish a adjudication process for disagreements and iterate on guidelines.
Build a representative, version-controlled golden set with high-quality labels, ideally from expert annotators. Periodically refresh it to reflect current data distributions and re-evaluate model performance.
Track label distributions over time and across segments (query intent, locale) using statistical tests (e.g., chi-square, PSI). For Simpson's paradox, analyze metrics at both aggregate and segment levels, and use visualization (e.g., scatter plots, heatmaps) to detect reversals.
When drift or paradox is detected, investigate root causes (e.g., changes in user behavior, labeling guidelines, or segment mix). Adjust the pipeline, retrain models, or reweight metrics as needed, and communicate findings to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.