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Tier-1 brand, metro location, and mid-level experience increase competition, but niche quant regulatory specialization limits applicants.
Specialized credit risk and regulatory modeling experience limits cross-industry transferability.
Explicit 4+ years quantitative experience, Master's degree, regulatory credit-model and Python/PySpark requirements create strict shortlisting filters.
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Create, implement, and document complex quantitative models using advanced statistical theory to analyze markets and forecast losses.
Manage market, credit, and operational risks to compute capital requirements and provide insights on business initiatives, including regulatory credit risk models (CCAR, CECL, IFRS).
Collaborate with regulators, auditors, and technical teams on analytical strategies, model deployment, monitoring, and risk assessments.
4+ years of Quantitative Analytics experience demonstrated via work experience, training, military experience, or education.
Master's degree or higher in quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science.
Experience in Python programming, model deployment frameworks, and credit risk modeling (including regulatory models like CCAR, CECL, IFRS).
Strong documentation and project management skills in a dynamic and complex environment.
Experienced in implementation and monitoring of regulatory credit risk and valuation models with deep knowledge of Python, PySpark, and model deployment.
Able to engage effectively with technical stakeholders, regulators, and auditors, providing clear communication and consultancy.
Capable of managing multiple priorities under pressure while ensuring adherence to risk and compliance requirements.