Model Risk Senior Analyst - Validation (AIML, GenAI, Technology)

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.

• →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.

• →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.

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

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.

• 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.