Quantitative Senior Principal

RESPONSIBILITIES

• Lead complex model risk management engagements, including independent model validation, quantitative model development, model risk assessments, and governance framework enhancements while maintaining appropriate independence between development and validation
• Provide technical leadership across statistical, econometric, financial risk, machine learning, and AI-based models
• Oversee validation activities including conceptual soundness, data quality, methodology evaluation, implementation testing, sensitivity analysis, benchmarking, outcomes analysis, and performance monitoring
• Lead modeling and validation work across credit risk, market risk, capital and stress testing, operational risk, financial crime, fraud, and fair lending
• Advise financial institutions on model governance, inventories, risk classification/tiering, validation methodologies, lifecycle management, remediation, and AI governance
• Design, develop, evaluate, and validate AI-enabled risk and compliance solutions, reusable analytical assets, and automation tools
• Contribute hands-on to technical design, prototyping, Python implementation, model integration, testing, and evaluation
• Assess AI risks including explainability, bias/fairness, robustness, hallucinations, privacy, security, human oversight, and monitoring
• Lead multidisciplinary client engagements, ensure technical quality, advise senior stakeholders, support proposals/presentations, mentor quantitative professionals, and contribute to methodologies and thought leadership

REQUIREMENTS

• Deep expertise in quantitative modeling, model risk management (MRM), financial services risk, regulatory compliance, and AI
• Strong knowledge of statistical, econometric, financial risk, machine learning, and AI-based models
• Experience with model development and independent validation, including conceptual soundness, data quality, methodology evaluation, implementation testing, sensitivity analysis, benchmarking, outcomes analysis, and performance monitoring
• Expertise relevant to financial risk/compliance domains such as credit risk, market risk, capital and stress testing, operational risk, financial crime, fraud, and fair lending
• Knowledge of model risk governance, inventories, risk classification/tiering, validation methodologies, model lifecycle management, and remediation
• AI/ML expertise including machine learning, Generative AI, large language models (LLMs), and agentic AI systems
• Hands-on technical capability in Python-based implementation, prototyping, model integration, testing, and evaluation
• Understanding of AI risks and controls: explainability, bias/fairness, robustness, hallucinations, data privacy, security, human oversight, and monitoring
• Knowledge of banking regulations and supervisory guidance, including revised U.S. interagency model risk management guidance (SR 26-2), where applicable
• Proven consulting leadership with the ability to lead complex engagements, provide independent technical challenge, advise senior stakeholders, support business development, and mentor quantitative professionals