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Strong bank brand, metro location, and mid-level demand but specialized quantitative credit skills limit applicant density.
Strong banking credit risk and regulatory data requirements limit cross-industry transferability.
Multiple explicit mandatory skills and 5+ years domain experience make shortlisting highly selective.
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Lead complex quantitative initiatives including creation, implementation, validation, and defense of statistical models and theories.
Qualify, monitor markets, forecast credit and operational risks, and provide analytical support for business initiatives with broad impact.
Collaborate with regulators and auditors, review models from technical, audit, and market perspectives, and enable decision-making for product and marketing strategies.
5+ years of Quantitative Analytics experience in credit risk analytics with expertise in statistical and machine learning model development or ML Ops.
Bachelor's degree or higher in a quantitative discipline (e.g., mathematics, statistics, engineering, physics, economics, computer science).
Advanced programming skills: 5+ years in Python including OOP and deployment; 2+ years in SAS and experience with HPC, Big Data (PySpark, MapR), and real-time solutions.
Experience with unit testing, UAT, regression testing, code review, Git/GitHub, CI/CD pipelines, UNIX commands, and familiarity with banking domain credit risk on Retail/Commercial portfolio.
Experienced quantitative analyst with a strong background in credit risk, machine learning, and model lifecycle management in financial services.
Technically skilled in advanced Python programming, big data environments, and production-ready ML solutions specialized for banking risk analytics.
Capable of operating in a regulated environment, interfacing with auditors/regulators, and driving analytical projects that influence strategic credit risk decisions.