M&T Bank is looking to solve credit risk, interest rate risk, and liquidity risk management problems through the development and analysis of quantitative/econometric behavioral models.
Requirements
- Minimum of 1 years’ on-the-job experience with pertinent statistical software packages (SAS, Python, Stata, R), especially SAS & Python.
- Credit Risk Modeling experience
- Logistic regression in credit risk modeling experience
- Time Series Analysis & Monte Carlo simulation experience.
- Minimum of 1 years’ on-the-job experience with data management environment, such as SQL Server Management Studio
- Minimum of 1 years’ experience in managing and analyzing large data sets and explaining results of analysis through concise written and verbal communication as well as charts/graphs
- Fluency and high proficiency in econometric/statistical techniques, especially time-series analysis, panel data methods and logistic regression
Responsibilities
- With experienced skillset, assist in researching and developing quantitative behavioral models used for credit risk, interest rate risk and liquidity risk management, as well as balance sheet and capital planning, including but not limited to, loan delinquency, default and loss models, loan prepayment and utilization models, deposit attrition models and financial instrument valuation methods.
- Prepare, manage and analyze large customer loan, deposit and/or financial data sets for statistical analysis in Structured Query Language (SQL) or similar tool to properly specify and estimate econometric models to understand customer or Bank behavior for purposes of credit, interest rate, liquidity or stressed capital risk management.
- Run regressions (including time series and logistic regression), programming routines and other econometric analyses to specify models using appropriate statistical software; communicate results, including graphic and tabular forms, to fellow team members, Treasury management and Bank-wide stakeholders, including the business lines and Risk Management colleagues to demonstrate key risk drivers and dynamics of model output.
- Execute models in production environment; communicate analytical results to Bank-wide stakeholders.
- Track portfolio performance, model performance, campaign tracking and risk strategy results. Incorporate observations and data into existing models to improve predictive results. Identify deviations from forecast/expectations and explain variances. Identify risk and/or opportunities.
- Develop and maintain satisfactory model documentation, including process narratives and performance monitoring guidelines to serve as reference source.
- Provide financial analysis and data support to other groups/departments across the Bank as required. Support engagements with colleagues in Model Risk Management for model validation exercises.
Other
- Bachelor’s degree and a minimum of 1 years’ proven quantitative behavioral modeling experience, or in lieu of a degree, a combined minimum of 5 years’ higher education and/or work experience, including a minimum of 1 years’ proven quantitative behavior modeling experience
- Masters’ of Science or Doctorate degree in Statistics, Economics, Finance or related field in the quantitative social, physical, or engineering sciences, with proven coursework proficiency in statistics, econometrics, economics, computer science, finance or risk management
- Financial Risk Manager (FRM) or Chartered Financial Analyst (CFA) designation
- Demonstrated leadership skills
- Strong desire to learn and contribute to a group
- Hybrid position requiring in-office work three (3) days every week
