Credit Model Development Quantitative Manager

Credit Model Development Quantitative Manager

Requirements

• Bachelor’s degree in statistics, economics, finance or related field in the quantitative social, natural, physical or engineering sciences, with proven coursework proficiency in statistics, econometrics, economics, computer science, finance or risk management and a minimum of 6 years’ proven quantitative behavioral modeling experience (inclusive of a minimum of 2 years’ supervisory, management and/or work leadership experience), or in lieu of a degree, a combined minimum of 10 years’ higher education and/or work experience, including a minimum of 6 years’ proven quantitative behavioral modeling experience (inclusive of a minimum of 2 years’ supervisory, management and/or work leadership experience)
• Proven experience managing and analyzing large data sets and explaining results of analysis through concise written and verbal communication as well as charts/graphs
• Strong leadership skills; manager of people & project management

Responsibilities

• Lead teams in research and end-to-end development of quantitative models used for credit risk, including but not limited to, loss forecasting (loan delinquency, default and loss, loan prepayment, utilization, etc), capital planning (CCAR) CECL and/or underwriting
• Utilize next gen quantitative approaches (AI/ML), programming routines and other econometric analyses to facilitate the model development effective challenge process communicate results, including graphic and tabular forms, to fellow team members, stakeholders, including the business lines and Risk Management colleagues to demonstrate key risk drivers and dynamics of model output.
• Manage satisfactory model documentation, including process narratives and performance monitoring guidelines to serve as reference source.  Lead engagements with colleagues in Model Risk Management for model validation exercises.
• Work with a wide range of internal customers, including executive management, to explain the benefits, limitations, assumptions and requirements for proposed models, scorecards and forecasts.
• Work closely with internal and external business partners to develop and implement strategies for optimal pricing, underwriting or funding strategy with the end goal of maximizing firm profitability.
• Develop and implement performance metrics, reporting and analyses using models to support data-driven decision-making and forecasting for the firm’s customers, products and portfolios.
• Manage knowledge of Bank-specific and industry data sources necessary to support quantitative analytical and modeling efforts.  Serve as liaison across Bank-wide stakeholders to ensure appropriate data sourcing for projects.
• Serve as subject matter expert for on all facets of quantitative risk management and guide junior analysts and data scientists during the model development process.
• Mentor and supervise work of less experienced team members and assist in development of their technical and professional acumen.
• Define, develop and deploy best risk management practices and infrastructure Bank-wide.
• Exercise usual authority of a manager concerning staffing, performance appraisals, promotions, salary recommendations, performance management and terminations.
• Understand and adhere to the Company’s risk and regulatory standards, policies and controls in accordance with the Company’s Risk Appetite. Design, implement, maintain and enhance internal controls to mitigate risk on an ongoing basis.  Identify risk-related issues needing escalation to management.
• 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.

Preferred Qualifications

• Knowledge and familiarity with key aspects of model development for behavioral/quantitative models, including time series, scorecard, logistic regression, financial valuation or panel data models for credit risk, interest rate risk or liquidity risk management
• 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
• Experience with pertinent statistical software packages (e.g. SAS, Python, Stata, R)
• Experience with data management environment, such as SQL Server Management Studio
• CCAR and/or CECL experience