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Tier-1 bank brand and metro location increase competition, but niche regulatory credit-risk specialization lowers applicant breadth.
Strong banking and regulatory credit-model expertise yields low transferability across industries.
Mandatory 4+ years, regulatory credit-model experience, and required Python/PySpark make screening highly selective.
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Develop, implement, and document complex quantitative models related to market, credit, and operational risks to forecast losses and compute capital requirements.
Provide expertise on structured securities and statistical theory, influencing global technical, audit, and market assessments.
Collaborate with regulators, auditors, and technically oriented stakeholders to discuss analytical strategies, modeling, and forecasting methods.
Minimum 4+ years of Quantitative Analytics experience or equivalent through work experience, training, military experience, or education.
Master's degree or higher in a quantitative discipline (mathematics, statistics, engineering, physics, economics, or computer science).
Experience with credit risk modeling including regulatory models (CCAR, CECL, IFRS), and model implementation, production, monitoring, and analytics.
Advanced programming expertise (4+ years) in Python, PySpark, and model deployment frameworks.
Demonstrated expertise in credit risk modeling and regulatory model implementation with strong documentation and project management skills.
Ability to collaborate across business and functional areas as well as communicate effectively with technical and regulatory stakeholders.
Experience working in dynamic, complex environments requiring prioritization and managing deadlines under pressure.