ML Interview Mistakes Quant Candidates Make
Machine learning interview mistakes quant candidates make, covering leakage, weak baselines, wrong metrics, overfitting, feature claims, and communication.
Candidates who know ML vocabulary but need sharper interview judgment.
Mistake one: ignoring leakage
Leakage can make a model look excellent while being unusable. Always explain feature timing, label construction, and validation splits.
Mistake two: skipping baselines
A complex model should beat simple baselines, not just produce a metric. Compare against mean predictions, linear models, simple rules, or existing heuristics.
Concrete example
If XGBoost improves test loss slightly but increases turnover dramatically, it may be worse than a simpler model after costs.
Mistake three: overclaiming features
Feature importance, correlations, and backtest results are evidence, not proof. State uncertainty and the checks that would increase confidence.
Common mistakes
Candidates often sound confident but vague. Precise caveats, clean definitions, and simple validation logic usually beat broad claims.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.