Credit Model Development Quantitative Analyst II - Small Business and Secured; Hybrid - se

Work Location/Arrangement

Hybrid position requiring in-office work four (4) days every week at an M&T office in Buffalo, NY, Bridgeport, CT, Wilmington, DE, Baltimore, MD, Washington, DC, or possibly NY, NY. If the final candidate is not near one of the attached locations, there might be a possibility for a remote arrangement.

Provides analytical and technical support for the development, refinement, and ongoing monitoring of credit risk models used to meet regulatory requirements and support the Bank’s strategic risk management objectives. This includes models for loss forecasting, default probability estimation, and other credit‑sensitive behaviors across lending portfolios. Performs data preparation, exploratory data analysis, and model estimation under the guidance of senior modelers, leveraging strong quantitative skills and proficiency in Python, SQL, and statistical methods.

Collaborates with Credit Risk Management, Model Risk Management, and business line partners to ensure model methodologies, assumptions, and outputs align with regulatory expectations and the Bank’s broader credit risk framework. Communicates analytical results through clear narratives, visualizations, and documentation that support model development, validation activities, and ongoing performance monitoring.

• Support the development, enhancement, and testing of credit risk models, including probability of default, loss forecasting, risk rating, and other borrower‑behavior models.
• Conduct statistical and econometric analyses using Python, SQL, and related tools to estimate, validate, and refine model components.
• Prepare, clean, and analyze large‑scale loan and customer datasets, ensuring data quality and readiness for modeling.
• Assist with model implementation and ongoing performance monitoring, identifying deviations and contributing to model improvements.
• Develop and maintain clear, comprehensive model documentation and performance monitoring reports.
• Communicate analytical findings through visualizations, presentations, and written summaries.
• Collaborate with Credit Risk Management, Model Risk Management, and business partners to ensure model alignment with regulatory expectations.
• Provide analytical support across the Bank and contribute to a collaborative, results‑focused environment.

The position serves as a mid‑level quantitative analyst responsible for applying statistical programming and analytical techniques to support the development, implementation, and maintenance of credit risk models. The analyst works with complex datasets and contributes to the creation of behavioral and credit‑sensitive models. The role requires clear communication of findings through narratives, visualizations, and technical explanations. Success requires strong attention to detail, consistent execution, and the ability to manage multiple concurrent initiatives in collaboration with teams across the Bank.

The analyst must be able to identify and interpret complex business, data, and statistical issues, contributing to solutions that enhance model performance and support broader risk management objectives.

Not Applicable

• Bachelor’s degree and a minimum of one year of proven quantitative behavioral modeling experience, or a combined minimum of five years of higher education and/or work experience, including at least one year of quantitative modeling experience.
• Minimum of one year of on‑the‑job experience using statistical software packages such as SAS, Python, or R.
• Strong Python skills required.
• Model development experience required, including familiarity with logistic and linear regression techniques.
• Minimum of one year of experience working in a data management environment such as SQL Server Management Studio.
• Minimum of one year of experience managing and analyzing large datasets, with the ability to communicate results clearly using written, verbal, and visual formats.

• Master’s or Doctorate degree in Statistics, Economics, Finance, or a related quantitative field.
• Minimum of two…