Quant Interview Machine Learning Final Drill
Final machine learning drill for quant interviews covering labels, leakage, metrics, validation, drift, model explanation, and deployment caveats.
Candidates preparing for quant research, data, and machine-learning interview prompts.
Define labels and horizon
A quant ML answer should identify the prediction target, horizon, sampling unit, and whether the label is tradable after costs and timing constraints.
Search for leakage first
Before praising accuracy, inspect feature timing, target construction, universe selection, and preprocessing. Leakage can make a model look useful when it is impossible live.
Choose metrics for the decision
Accuracy, AUC, rank correlation, return, drawdown, turnover, and calibration answer different questions. Pick metrics that match the portfolio or research decision.
Validate through time
Time-series validation should respect chronology, regime shifts, and retraining cadence. Random splits are often inappropriate for market-like data.
Common mistakes
Candidates often optimize a generic model score. Strong ML answers connect data timing, objective, validation, costs, drift, and interpretability.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.