Start by defining the prediction problem and the point in time when predictions are made, then systematically audit each feature for temporal consistency and target leakage. Propose concrete fixes like time-based splits, feature lagging, and leakage detection tests, and emphasize validation with a holdout set that mimics production.
Pro tip: Always ask when the prediction is made relative to the data collection—if a feature is only known after the outcome, it's leakage. Use a time-based split and simulate production by training on past data and testing on future data.
Clarify the exact prediction point (e.g., at send time) and the target (e.g., purchased_within_7d). Map out when each feature becomes available relative to the prediction point.
Check each feature's timestamp: if it's recorded after the prediction point or after the outcome window starts, it's likely leakage. Look for features that directly encode the target (e.g., 'clicked' when predicting 'purchased').
Use correlation analysis, feature importance from a quick model, and permutation tests to spot suspiciously predictive features. Compare model performance with and without suspect features.
Remove or lag leaking features, use time-based splits, and ensure the training set only contains data available at prediction time. Validate on a holdout set that respects the temporal order.
After deployment, monitor for leakage drift and re-audit features periodically. Set up alerts for unexpected feature importance changes.
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Fairly broad but I appreciated that they bundled it all together rather than asking each separately.
Start by framing the answer around the business context—crypto trading data at Coinbase—and emphasize that EDA is iterative and tied to modeling goals. Then systematically address each issue (class imbalance, missingness, outliers, high-cardinality categoricals) with specific techniques, explaining how you'd diagnose and handle them while avoiding data leakage. Conclude by discussing how these EDA decisions inform feature engineering and model selection.
Pro tip: Always tie your EDA choices back to the business problem and model requirements—for example, in fraud detection, class imbalance might be handled with cost-sensitive learning rather than resampling, and high-cardinality categoricals might be encoded using target encoding with proper cross-validation to prevent leakage.
Begin by exploring the dataset's structure, types, and summary statistics, while clarifying the prediction goal and how each issue impacts the business (e.g., fraud detection, user churn). This guides which EDA techniques are most relevant.
Quantify class imbalance (e.g., ratio), missingness patterns (MCAR, MAR, MNAR), outliers (via statistical or visual methods), and cardinality of categorical variables. Use appropriate plots and metrics to assess severity.
Consider resampling (oversampling/undersampling), synthetic data generation (SMOTE), or algorithmic approaches (class weights, cost-sensitive learning). Evaluate impact using appropriate metrics like AUC-PR, F1, or recall.
For missingness, choose imputation (mean/median, model-based, or indicator variables) based on mechanism; for outliers, decide whether to remove, cap, transform, or use robust models, considering their potential signal.
Encode using target encoding, frequency encoding, or embeddings, being careful to avoid overfitting and leakage. Group rare categories or use domain knowledge to reduce dimensionality.
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The time-based split was the part I spent most time on.
Start by emphasizing the importance of time-based splits to prevent data leakage and simulate real-world deployment. Then outline a chronological split (e.g., train on earliest data, validate on middle, test on latest) and describe hyperparameter tuning using time-series cross-validation. Finally, discuss how to handle class imbalance and evaluate models with appropriate metrics like AUC-PR.
Pro tip: Use a rolling-origin or expanding-window cross-validation for hyperparameter tuning to respect temporal order, and always set aside the most recent data as a final holdout to estimate future performance.
Clarify the target: purchase within 7 days of a reference point (e.g., user signup or session start). Ensure features are computed only from data available before the prediction time to avoid leakage.
Split data chronologically: e.g., first 70% for training, next 15% for validation, last 15% for testing. Use the validation set for hyperparameter tuning and the test set for final evaluation.
Within the training set, use expanding-window or rolling-origin cross-validation to tune hyperparameters for both models. This respects temporal order and provides more reliable estimates.
For logistic regression, use class weights or resampling; for GBT, tune scale_pos_weight or use focal loss. Evaluate with AUC-PR, F1, or lift at a fixed threshold, not just accuracy.
Select the best hyperparameters based on validation performance, retrain on training+validation, and evaluate once on the held-out test set. Consider business metrics like expected profit or conversion lift.
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PR AUC matters more here given the imbalance, I made that point early.
Start by clarifying the business context and the specific decision the model will inform, then explain how each metric addresses different aspects of performance. Describe a systematic approach to compute confidence intervals and assess stability across subgroups, emphasizing the importance of uncertainty quantification and fairness in high-stakes domains like crypto.
Pro tip: In fintech, calibration and subgroup stability often matter more than raw discrimination metrics because they directly impact risk and user trust. Always tie your evaluation back to the business cost of errors and regulatory considerations.
Understand the model's purpose (e.g., fraud detection, user churn) and the costs of false positives/negatives. Identify any regulatory or fairness requirements that influence metric selection.
Explain when to prioritize ROC AUC (balanced classes), PR AUC (imbalanced classes), calibration (probability reliability), and incremental lift (campaign targeting). Discuss their complementary roles.
Use bootstrapping or analytical methods (e.g., DeLong for AUC) to estimate uncertainty. For calibration, consider confidence bands via bootstrapping or Bayesian approaches.
Define relevant subgroups (e.g., user geography, transaction volume) and compute metrics per subgroup with confidence intervals. Test for significant differences and investigate causes.
Summarize trade-offs, highlight risks from unstable subgroups, and recommend actions (e.g., recalibration, feature engineering, or model retraining).
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Expected value framing: score each user, multiply predicted uplift by avg cart value, subtract $0.003, send if positive.
Frame the threshold choice as an expected-value optimization problem: compare the cost of an email to the expected incremental value from the treatment effect, then set the threshold where expected profit is maximized. Discuss how to estimate the treatment effect from an experiment, account for uncertainty, and translate it into a deployment rule. Then outline a monitoring and retraining plan that tracks model performance, data drift, and business metrics, with clear triggers for retraining.
Pro tip: Emphasize that the optimal threshold depends on the business objective and risk tolerance, and that you would validate it with a holdout experiment before full deployment. Also mention that monitoring should include both model health and the causal effect over time, as treatment effects can decay.
Clarify the goal: maximize expected profit from sending emails, where profit = (treatment effect * value) - cost. Identify the unit of decision (e.g., user) and the available features for targeting.
Use experimental data (A/B test) to estimate the incremental effect of the email on the outcome (e.g., conversion). Quantify uncertainty (confidence intervals) and consider heterogeneity across user segments.
For each user, predict the probability of a positive response or the expected treatment effect. Set the threshold where expected incremental profit equals zero: P(response) * value * effect - cost = 0. Adjust for risk tolerance and business constraints.
Test the threshold in a holdout experiment to confirm the expected lift and profit. Deploy gradually, monitoring key metrics and guardrails (e.g., unsubscribe rates, customer satisfaction).
Continuously track model performance, data drift, and the causal effect over time. Set up automated alerts for degradation. Retrain periodically or when drift is detected, and re-evaluate the threshold with new experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.