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Tier-1 brand, metro location, and mid-level title increase competition despite niche regulatory specialization.
Strong banking regulatory and credit risk modeling focus limits cross-industry transferability.
Explicit 5+ years, mandatory regulatory credit-modeling experience, and specific tech stack make shortlisting highly strict.
Lead complex, large-scale quantitative model maintenance, optimization, and lifecycle initiatives for Retail credit risk portfolios.
Drive projects including implementation, monitoring, and execution of credit risk and stress testing models (CECL, IFRS9, Basel, CCAR) with measurable impact on model performance and compliance.
Develop and deliver analytics dashboards, documentation frameworks, and process improvements for predictive modeling and credit risk monitoring across retail business lines.
5+ years experience in quantitative model solutions or operations, particularly in credit risk analytics or credit risk modeling/monitoring/implementation.
Bachelor's degree from a premier institute or Masters/PhD in quantitative fields (applied mathematics, statistics, engineering, finance, economics, econometrics, or computer sciences).
Advanced programming skills in Python, SAS, and SQL; exposure to BI tools like Tableau or PowerBI for dashboarding.
Strong knowledge of regulatory models and compliance frameworks related to CCAR, CECL, IFRS9, RRP, and Basel.
Experienced in leading advanced quantitative analytics teams and cross-functional stakeholder management in credit risk modeling contexts.
Ability to handle complex, multi-faceted modeling and regulatory challenges within retail banking products such as Home Lending, Auto, Cards, and Personal Loans.
Demonstrated proficiency in project management, coding, data analysis, and providing consultative expertise on predictive analytics and credit risk subjects.