Lead Quantitative Credit Risk Modeler

M&T Bank Corporation; Buffalo (NY); Hybrid; USD 103,000 - 172,000; Full time

Job summary

M&T Bank Corporationis seeking a senior quantitative analyst in a hybrid role based in Buffalo, NY. You will develop, implement and maintain behavioral models for credit, liquidity and balance sheet risk, and lead teams on complex analytics projects.

You will work with data from customer loans and deposits, run time-series and logistic regressions, and present results to senior stakeholders. A strong background in statistics, econometrics and model risk is required.

Qualifications

• Bachelor’s degree with 4+ years of quantitative behavioral modeling experience.
• Fluent in open-source development: R or Python.
• End-to-end model development lifecycle experience.
• Experience working with model users and stakeholders for feedback.
• Experience leading projects and coordinating teams.
• 4+ years with SAS, Python, Stata or R in on-the-job settings.
• Experience with SQL Server Management Studio for data management.

Responsibilities

• Lead research and development of quantitative behavioral models for credit risk, interest rate risk and liquidity risk.
• Prepare and analyze large loan/deposit datasets for econometric modeling.
• Communicate model results to Treasury and risk stakeholders with visuals and narratives.
• Run regressions and other econometric analyses in appropriate software.
• Deploy models in production and monitor performance and risk signals.
• Lead model validation engagements with Model Risk Management.

Skills

R

Python

Education

Bachelor’s degree + 4+ years modeling

Master’s or PhD in statistics/economics/finance

Tools

SQL

SAS

Stata

Git

Job description

M&T Bank Corporationis seeking a senior quantitative analyst in a hybrid role based in Buffalo, NY. You will develop, implement and maintain behavioral models for credit, liquidity and balance sheet risk, and lead teams on complex analytics projects.

You will work with data from customer loans and deposits, run time-series and logistic regressions, and present results to senior stakeholders. A strong background in statistics, econometrics and model risk is required.