Overview: The Senior Model Validation Analyst is responsible for executing robust, independent validations of quantitative and qualitative models across the enterprise. This role serves as a key control function within Model Risk Management (MRM), ensuring models are conceptually sound, empirically validated, and compliant with regulatory and internal standards.

Primary Responsibilities:

• Lead end-to-end validation of several model families, with a focus on Gen-AI and AI/ML Models. Experience working with or validating cybersecurity models is preferred.
• Conduct the validation and analysis of expert judgment or qualitative factors that augment quantitative models; review to confirm proper controls and adequate documentation are in place.
• Perform independent challenge of model methodologies, benchmarking, back-testing, sensitivity analysis, and stress testing.
• Maintain high-quality documentation of validation work, findings, and conclusions to withstand internal audit and regulatory scrutiny.
• Maintain M&T internal control standards, including timely implementation of internal and external audit points together with any issues raised by external regulators as applicable.
• Support remediation of validation, audit, and regulatory findings.
• Partner with model developers, business stakeholders, and risk managers to communicate validation outcomes, challenge assumptions, and recommend improvements.

Scope of Responsibilities:

• Independently manage multiple validation projects.
• Partner with business lines including Technology and Cybersecurity, Credit Risk, Finance, and Private Wealth.
• Balance regulatory expectations with business objectives.
• Contribute to continuous improvement of validation practices and governance.

Supervisory/Managerial Responsibilities:

Individual contributor with opportunities to mentor junior analysts and provide technical guidance.

Education and Experience Required:

Master's or Doctoral Degree in Mathematics, Statistics, Business Engineering, Econometrics, or Science-based discipline,

Plus 4 years’ experience in model development or validation, with a combined minimum of >5 years’ higher education and relevant work experience.

Technical knowledge of advanced software packages used in analytics.

Education and Experience Preferred:

• Master's or PhD in a quantitative discipline (Computer Science, Software Engineering, Statistics, Mathematics).
• 5–10+ years in model validation, development, or quantitative analytics.
• Strong knowledge of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Natural Language Processing (NLP).
• Good knowledge of model risk, SR 11-07, SR 26-2, and regulatory expectations.
• Proficiency in Python, PyTorch, Git, R, or similar tools.
• Strong analytical, communication, and stakeholder management skills.

Location