Machine Learning Quant Interview Guide
Machine learning quant interview guide covering supervised learning, validation, leakage, feature design, evaluation, and practice planning.
Candidates preparing for research, data science, and systematic trading interviews.
ML questions test judgment
Quant machine learning interviews are usually less about naming algorithms and more about defining data, labels, validation, leakage controls, and realistic evaluation.
Start with the prediction setup
State the target, horizon, features available at decision time, sample unit, and loss or utility. A vague setup makes every later answer weaker.
Concrete example
For a return prediction model, define whether the label is next-day return, risk-adjusted return, direction, or rank. Then choose validation that respects time.
Validation carries the answer
Discuss train/test splits, cross-validation limits, transaction costs, benchmark comparisons, and checks for label leakage before celebrating model performance.
Common mistakes
Candidates often lead with model names. Lead with the data-generating process, constraints, and how you would know the model is genuinely useful.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.