Structure your answer around the full ML lifecycle, emphasizing how you handle delayed labels and class imbalance while aligning with business constraints like review capacity. Start with data understanding and feature engineering, then move through training, deployment, and monitoring, explicitly addressing each component in the question. Use concrete examples and trade-offs to demonstrate practical experience.
Pro tip: Quantify the impact of your design choices—e.g., how thresholding based on review capacity directly affects precision/recall and operational cost—and mention how you'd validate with backtesting using time-based splits to avoid leakage.
Explore the multi-table dataset, join transactions with customer/merchant metadata, and engineer features that capture fraud patterns (e.g., velocity, deviation from norms, graph-based features). Handle missing values and ensure features are available at scoring time.
Address delayed/partial labels by using techniques like PU learning, label propagation, or time-windowed labeling. Mitigate class imbalance with resampling, class weights, or anomaly detection methods, and evaluate using metrics robust to imbalance (e.g., PR-AUC).
Use time-based splits to mimic real-world deployment and avoid leakage. Train models (e.g., gradient boosting, neural networks) with proper validation, and calibrate probabilities. Consider ensemble or online learning for adaptability.
Set thresholds based on review capacity and business costs (e.g., cost-sensitive learning). Deploy a low-latency scoring service with feature store integration, and implement a tiered review system (auto-approve, manual review, auto-block).
Incorporate analyst feedback to update labels and retrain models. Monitor data drift, model performance, and business metrics. Backtest on historical data with time-based splits to simulate production and estimate impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.