This position supports the development of statistical and machine learning models within a regulated environment. The role contributes to key components of the model development lifecycle—including data preparation, model development, testing/validation support, implementation, and monitoring—while working closely with senior modelers to deliver analytically sound and well-documented solutions. Development work is performed using both Python and SAS.
Support the design, development, testing, implementation, and monitoring of statistical and machine learning models
Prepare, transform, and analyze large datasets (e.g., transactions, customer behavior, entity data)
Partner with senior team members to translate business problems into analytical approaches
Contribute to model documentation, including methodology, assumptions, and monitoring frameworks
Develop and maintain analytical code using Python and/or SAS
Assist in model performance monitoring and identification of model issues or limitations
Support model validation, audit, and regulatory review processes
Collaborate with stakeholders across EDD, FIU, Technology, and Model Risk Management
Ensure adherence to internal controls and regulatory expectations
Provide guidance to junior analysts where appropriate
Understand and adhere to the Company’s risk and regulatory standards, policies and controls in accordance with the Company’s Risk Appetite. Identify risk-related issues needing escalation to management.
Promote an environment that supports belonging and reflects the M&T Bank brand.
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.
Complete other related duties as assigned.
Works under general guidance from more senior quantitative risk managers. Responsible for independent execution of defined analytical tasks and contributing to model development efforts. Builds technical expertise in AML modeling and governance.