Profile anonymized lake; interrogate large tables; ensure stats traceable to source.
Build and validate core risk models: PD, delinquency, early-warning, segmentation, scorecards.
Establish model validation discipline: train/test/off-time splits; Gini/AUC/KS; backtesting.
Define feature logic with Data Engineer; write SQL/dbt/Python; harmonized semantics.
Prototype NL insight layer: text-to-SQL / RAG; add guardrails.
Lead a small pod; manage tasks; present findings to client leadership.

🎯 Requirements

Expert Python for DS (pandas/Polars, scikit-learn, statsmodels) + SQL.
Credit-risk/financial modeling: scorecards, PD, delinquency, segmentation, governance.
Data validation, profiling and feature engineering on enterprise data.
dbt/semantic modeling; collaborate with data engineering on harmonization layer.
GenAI insight layer: text-to-SQL, RAG over data; guardrails.
7+ years hands-on DS in regulated contexts; lead small teams; hands-on coding.

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