This is where I spent the most time and probably rambled a bit.
Start by systematically diagnosing the root causes of zeros and missing values, distinguishing between true zeros, missing at random, and missing not at random. Then, for each case, apply appropriate handling strategies during modeling, such as imputation, feature engineering, or model-based approaches, while validating the impact on model performance.
Pro tip: Always investigate the data generation process first—zeros and missing values often carry business meaning (e.g., zero usage vs. missing data). Document your assumptions and test multiple handling strategies to see which yields the best model performance.
Examine how the data was collected and what zeros and missing values represent in the business context. For example, a zero might mean 'no activity' while missing might mean 'data not tracked'.
Analyze the distribution of zeros and missing values across features, time, and segments. Use visualizations and statistical tests to determine if they are random or systematic.
Categorize missing values as Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR). This guides the appropriate handling strategy.
For true zeros, consider leaving as is or transforming; for MCAR/MAR, use imputation (mean, median, model-based); for MNAR, use domain-specific imputation or include missingness indicators.
Test the impact of each handling method on model performance using cross-validation. Monitor for bias and adjust as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The training-serving skew angle was the part I found genuinely tricky.
Start by clarifying the business impact and constraints: how critical is the feature, what's the cost of dropping it, and what's the latency/accuracy trade-off. Then systematically evaluate each option (drop, proxy, reframe) against data leakage risks, implementation complexity, and stakeholder needs. Conclude with a recommendation that balances model performance with product feasibility, and suggest validation via offline simulation or A/B test.
Pro tip: Emphasize that you'd quantify the feature's importance (e.g., permutation importance) and simulate the impact of each option before deciding—this shows you're data-driven and mitigate risk. Also, mention that reframing the prediction time might require cross-team alignment, so loop in product and engineering early.
Ask questions to understand why the feature is unavailable at inference (e.g., latency, data pipeline, regulatory) and what the business impact of losing it is. Identify success metrics and constraints like latency, cost, and stakeholder expectations.
Quantify how much the feature contributes to model performance using importance scores or ablation tests. Check if using it in training but not inference causes leakage or training-serving skew.
For each option, estimate impact on model accuracy, implementation effort, and maintainability. Dropping is simplest but may hurt performance; a proxy requires finding a correlated, available feature; reframing (e.g., predicting earlier or later) changes the prediction task and may need product buy-in.
Select the option that best balances performance, feasibility, and stakeholder needs. Validate offline with a holdout set simulating inference conditions, and if possible, run an online A/B test to measure real impact.
Document the decision, rationale, and expected impact for stakeholders. Monitor post-deployment and be ready to iterate if the chosen approach underperforms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
More sessions meaning less success sounds backwards at first.
Start by clarifying the metric and the correlation context, then propose at least two distinct explanations—one causal (feature harms success) and one non-causal (confounding or reverse causality)—and explicitly contrast them. For each, describe a validation method such as a controlled experiment, stratified analysis, or instrumental variable, and explain how you'd rule it out.
Pro tip: Acknowledge that correlation doesn't imply causation, but also note that negative correlation could be a sign of a valuable feature that attracts users who are already struggling—so investigate user intent and segment-level effects before concluding the feature is harmful.
Define what 'success' means (e.g., retention, revenue) and how the feature usage is measured. Confirm the correlation is negative and statistically significant, and consider the time window and population.
Hypothesize that using the feature directly causes worse outcomes (e.g., confusing UI, performance issues). Suggest validation via A/B test or randomized controlled trial, or by analyzing feature usage intensity and outcomes.
Offer an alternative: a third variable (e.g., user skill) drives both feature use and lower success, or struggling users seek out the feature (reverse causality). Validate by stratifying on confounders, using propensity score matching, or checking temporal order.
Explicitly compare the causal and non-causal explanations, noting that they imply different interventions. Describe how you'd rule out each: e.g., if A/B test shows no effect, causal explanation is weakened; if effect disappears after controlling for confounder, non-causal is supported.
Summarize which explanation is more likely based on evidence, and propose a path forward: iterate on the feature, target it to the right users, or deprecate it. Emphasize the importance of experimentation and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.