I went straight to online metrics (click-through rate, apply rate) and almost forgot to talk about offline evaluation entirely.
Start by framing the evaluation around the algorithm's objective—connecting qualified members to relevant jobs—and then propose a balanced set of online (behavioral) and offline (model-centric) metrics. Emphasize that online A/B test metrics are the ultimate arbiter, while offline metrics serve as early sanity checks and diagnostic tools.
Pro tip: Always tie metrics to the product's north-star (e.g., successful hires or qualified applications) and mention guardrail metrics to show you understand trade-offs, not just raw engagement.
Clarify what 'better' means for the job recommendation algorithm—likely increasing qualified applications, interviews, or hires while maintaining member trust and engagement.
Choose behavioral metrics from A/B tests such as click-through rate, apply rate, qualified apply rate, and downstream outcomes like interview or hire rate, plus engagement metrics like job views and saves.
Use model-centric metrics like precision@k, recall@k, NDCG, MAP, and coverage/diversity to assess ranking quality and relevance before or alongside online tests.
Monitor for negative side effects: unsubscribe rates, hide/block actions, report rates, and long-term member satisfaction to ensure the algorithm doesn't harm the ecosystem.
Run a rigorous A/B test with sufficient power, analyze primary and secondary metrics, and check for novelty effects or segment-level differences before concluding superiority.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the goal and metrics, then discuss the challenge of network effects and propose methods like cluster-based randomization or ego-network experiments. Emphasize the need to measure both direct and spillover effects, and suggest using a combination of online and offline evaluation.
Pro tip: At LinkedIn, network effects are significant; consider using graph clustering to create balanced treatment and control groups, and always check for interference by comparing within-cluster and across-cluster effects.
Clearly state the primary metric (e.g., click-through rate, engagement) and guardrail metrics (e.g., user satisfaction, revenue). Consider both user-level and network-level metrics.
Explain how interactions between users (e.g., connections, shared content) can cause spillover effects, violating the Stable Unit Treatment Value Assumption (SUTVA).
Propose randomizing at a higher level than the user, such as clusters of connected users or ego-networks, to minimize interference. Discuss trade-offs between cluster size and statistical power.
Outline the experiment design: cluster randomization, sample size calculation accounting for intra-cluster correlation, and analysis using methods like cluster-robust standard errors or causal inference techniques.
Suggest validation through A/A tests, checking for interference, and potentially running a switchback or time-based experiment. Consider combining with offline evaluation or simulation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through power calculations, minimum detectable effect, and variance.
Start by framing the problem around the experiment's goal: define the primary metric, minimum detectable effect (MDE), and statistical power. Then explain how to compute sample size using power analysis, translate that into test duration based on traffic, and choose traffic allocation to balance risk and speed. Emphasize that these decisions are interdependent and should be revisited as the experiment progresses.
Pro tip: At LinkedIn, where network effects and long-term user value matter, always consider whether the experiment could have spillover effects or delayed impact—this may require cluster randomization or longer run times. Also, be prepared to discuss how you'd handle multiple testing corrections if you're evaluating several metrics.
Clarify the hypothesis and choose a single primary metric that directly measures success. Identify guardrail metrics to ensure the change doesn't harm other key areas.
Work with stakeholders to agree on the smallest effect size that would be practically significant. This depends on business impact and the cost of false positives/negatives.
Use power analysis (e.g., with alpha=0.05, power=0.8) to calculate the sample size needed to detect the MDE, given the metric's variance. Consider using tools like Evan Miller's calculator or Python libraries.
Based on daily traffic eligible for the experiment, calculate how many days are needed to reach the required sample size. Decide on traffic allocation (e.g., 50/50) balancing statistical power, risk, and speed; consider a ramp-up plan.
During the test, monitor for data quality, sample ratio mismatch (SRM), and early signals. If needed, extend the test or adjust traffic, but avoid peeking and stopping early without proper corrections.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the need to validate the observation and quantify the performance disparity across segments. Then systematically investigate potential causes—data, model, and user behavior—and propose targeted next steps, including further experimentation and iteration.
Pro tip: Emphasize the importance of understanding the business impact and prioritizing segments based on value and size. Also, mention the need to check for Simpson's paradox, where overall performance might mask segment-level issues.
Confirm the performance disparity by analyzing metrics across segments, ensuring statistical significance and checking for data quality issues or confounding variables.
Examine each segment's characteristics, data distribution, and model predictions to identify patterns or anomalies that could explain the performance gap.
Brainstorm potential causes: data representation, feature relevance, model bias, or user behavior differences. Prioritize hypotheses based on likelihood and impact.
Design experiments or analyses (e.g., A/B tests, bias audits, feature importance) to validate or refute each hypothesis, using holdout sets or targeted interventions.
Based on findings, propose next steps: retrain with balanced data, add segment-specific features, adjust model thresholds, or run targeted experiments to improve performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.