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Explain PD model validation steps

Wells FargoData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

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Explain PD model validation steps evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Explain how you would validate a newly developed PD model. Discuss data partitioning, discrimination metrics (e.g., AUC, KS), calibration checks, stability monitoring (e.g., PSI), backtesting, challenger models, and documentation.