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Tier-1 bank, metro location, and mid-level role increase competition despite niche regulatory credit-risk specialization.
Highly specialized regulatory credit-risk modeling experience limits transferability across industries.
Explicit 5+ years requirement plus mandatory credit-risk model, Python/PySpark, and regulatory implementation skills.
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Lead complex and large-scale model maintenance, optimization, and planning initiatives across operational processes including controls, reporting, testing, implementation, and documentation.
Make decisions and develop strategies for model processes and optimization addressing business needs and regulatory compliance within the System Development Life Cycle framework.
Collaborate globally with peers, management, and cross-functional teams to resolve issues, implement models (particularly regulatory credit risk models PD, LGD, EAD), and deliver scalable data pipelines using Python and PySpark.
5+ years of quantitative model solutions or quantitative model operations experience (work experience, training, military experience, or education).
Strong programming skills in Python and PySpark with experience in model implementation/development within banking or financial services.
Experience with regulatory credit risk models including CCAR, CECL, IFRS, RRP Valuation, and PPNR models.
Familiarity with version control (Git), CI/CD pipelines, and cloud platforms.
Experienced in end-to-end implementation of regulatory credit risk models into production systems in a banking or financial services environment.
Capable of designing and optimizing scalable data pipelines using distributed computing frameworks, demonstrating strong hands-on programming skills.
Able to provide technical leadership and mentorship, effectively collaborating across model development, validation, and business teams within a compliance-driven and risk-managed environment.