
Credit Model Quantitative Lead (Hybrid)
- Buffalo, NY
- $97,870-163,116 per year
- Permanent
- Full-time
- Work Arrangement/Location: This is a hybrid position requiring in-office work three days every week. Ideally the position will be based in Buffalo, NY but may be in an M&T office in Buffalo, NY, Baltimore, MD, Wilmington, DE, or Washington, DC.
- Develop and/or lead the development of quantitative models used for credit risk, capital planning or underwriting. This includes CCAR and CECL models and underwriting scorecards.
- Lead less experienced model developers and analysts as required to meet project objectives.
- Use Python, SAS and SQL to manipulate customer loan or financial data for statistical analysis and model development.
- Employ common model methodologies such as logistic regression, time series, survival analysis, boosted trees and similar machine learning methods to create robust and flexible solutions to complex business problems.
- Work with multiple model stakeholders across different areas of the bank to create solutions that meet their business needs. Use a full array of communication skills and visual analytics to obtain business partner requirements, present analyses, explain complex models to non-technical partners, and respond to enquiries from stakeholders.
- Write comprehensive and easily readable model documentation to enable Model Risk Management and stakeholders to review all aspects of model development, including justification of model methodologies chosen, candidate models, model performance.
- Conduct business in compliance with regulatory guidance including SR (Supervision and Regulation Letters) 10-1, SR 10-6, SR 11-7, Enhanced Prudential Standards, etc. Adhere to applicable compliance/operational/model risk controls and other standards, policies and procedures.
- Complete other related duties as assigned.
- Proven experience managing and analyzing large data sets and explaining results of analysis through concise written and verbal communication as well as charts/graphs
- Bachelor’s degree and a minimum of 4 years’ proven quantitative behavioral modeling experience, or in lieu of a degree, a combined minimum of 8 years’ higher education and/or work experience, including a minimum of 4 years’ proven quantitative behavioral modeling experience
- Minimum of 4 years’ on-the-job experience with pertinent statistical software packages; experience in SAS required.
- Minimum of 4 years’ on-the-job experience with data management environment, such as SQL Server Management Studio
- Proven experience managing and analyzing large data sets and explaining results of analysis through concise written and verbal communication as well as charts/graphs.
- Model development experience.
- Financial services/banking industry experience required.
- Masters’ of Science or Doctorate degree in statistics, computer science, engineering, economics, finance or related fields.
- Expertise in Python, SAS and SQL; experience rewriting SAS into Python is ideal.
- Model development experience in financial services, notably for common methodologies such as logistic regression, time series, survival analysis, boosted trees and similar machine learning methods.
- Experience with the development of underwriting scorecards and/or CCAR/CECL models is greatly valued.
- Logistical regression is highly preferred.
- PD & LGD models experience
- Knowledge and familiarity with key aspects of model risk management and model validation, including SR-11-7 guidance on model risk management
- Proven track record for being able to work autonomously and within a team environment
- Strong desire to learn and contribute to a group
- Previous experience leading and directing the work of less experienced personnel