Model Risk Senior Analyst - Validation (AI, Cyber, Technology)

M&T Bank•Buffalo, NY

1d•$103,000 - $171,600•Onsite

About The Position

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.

Requirements

Master's or Doctoral Degree in Mathematics, Statistics, Business Engineering, Econometrics, or Science-based discipline
4 years’ experience in model development or validation
Combined minimum of >5 years’ higher education and relevant work experience.
Technical knowledge of advanced software packages used in analytics.

Nice To Haves

Master's or PhD in a quantitative discipline (Finance, Economics, Statistics, Mathematics, Engineering).
7–10+ years in model validation, development, or quantitative analytics.
Strong knowledge of model risk, SR 11-07, SR 26-2, and regulatory expectations.
Proficiency in Python, SAS, R, or similar tools.
Strong analytical, communication, and stakeholder management skills.

Responsibilities

Lead end-to-end validation of several model families including Consumer CCAR and CECL credit risk models, AI/ML models, Cybersecurity and Technology models.
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 Credit Risk, Finance, Technology, and Wealth.
Balance regulatory expectations with business objectives.
Contribute to continuous improvement of validation practices and governance.