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Strong bank brand and metro location but niche credit-risk model validation reduces applicant density.
Requires deep banking credit-risk and regulatory model-validation expertise, limiting cross-industry transferability.
Explicit 4+ years, master’s degree, regulatory model-validation and Python/PySpark requirements create strict filters.
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Lead complex quantitative analysis including model creation, implementation, and documentation for commercial credit and corporate economic group models.
Conduct comprehensive model validations covering methodology, data integrity, performance, and regulatory compliance, communicating findings to auditors, regulators, and stakeholders.
Manage forecasting of losses and capital requirements across market, credit, and operational risks, applying advanced statistical and econometric techniques.
4+ years of quantitative analytics or equivalent experience.
Master's degree or higher in a quantitative discipline such as mathematics, statistics, economics, computer science, or engineering.
Hands-on programming experience in Python, Pyspark, and related libraries; proven ability in model validation and technical documentation.
Experience with credit risk model validation/development (PD, LGD, EAD models) and knowledge of regulatory requirements including SR26-2, CCAR, CECL, IFRS9.
Deep domain expertise in commercial/wholesale credit risk, including balance forecasting, loss forecasting, PPNR/fee models, and econometric methods.
Strong capability in quantitative model validation and development for credit portfolios with experience in scenario analysis, backtesting, and sensitivity analyses.
Proficient in applying regulatory guidelines in model risk management; able to handle complex data environments and communicate technical findings effectively to multiple stakeholders.