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Tier-1 brand, mid-level role in a metro with common ML/data skill requirements drives high competition.
Role requires regulatory credit risk modeling and banking experience, limiting transferability across industries.
Explicit 5+ years, regulatory credit risk mandates and specific tech stack make shortlisting highly strict.
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Lead complex model maintenance, optimization, and planning initiatives covering operational processes, controls, reporting, testing, implementation, and documentation.
Make decisions in multifaceted scenarios involving agile development and drive global assessment of model maintenance schedules aligned with SDLC, quality, security, and compliance.
Lead end-to-end implementation of regulatory credit risk models into production, design scalable data pipelines, and provide technical leadership and mentorship to junior members.
5+ years of quantitative model solutions or operations experience or equivalent via work, training, military, or education.
Experience in model implementation or quantitative analytics within banking or financial services (5+ years desired).
Strong programming skills in Python and PySpark.
Experience with regulatory credit risk models (CCAR, CECL, IFRS), familiarity with version control (Git), CI/CD pipelines, and cloud platforms.
Experienced in regulatory credit risk model implementation (PD, LGD, EAD) and deployment within large financial institutions.
Capable of leading complex, cross-functional initiatives involving model optimization and compliance aligned with regulatory standards (Basel, IFRS9, CCAR).
Effective technical leader with expertise in scalable data engineering (PySpark) and agile model lifecycle management.