Credit Model Development Quantitative Lead - Commercial Real Estate Portfolio(Hybrid)
M&T Bank · Buffalo, United States +6 · 1mo ago
*** Work Arrangement/Location: This is a hybrid position requiring in-office work four days every week. Ideally the position will be based in Buffalo, NY but may be in an M&T office in Baltimore, MD, Bridgeport, CT, NYC, NY, Iselin, NJ, Boston, MA, Wilmington, DE, Washington, DC, or possibly another M&T Bank corporate office.
There might be potential for a remote work arrangement depending upon the location of the final candidate .
Overview:
Independently develops, implements, maintains, analyzes and manages quantitative/econometric behavioral models used for credit risk, interest rate risk and liquidity risk management, as well as balance sheet and capital planning. May supervise the work of interns and/or lead teams, providing performance feedback to management as appropriate. Provides guidance and direction to less experienced personnel.
Primary Responsibilities:
- Lead research and development of 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, 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 the purposes of credit, interest rate, liquidity or stressed capital risk management. Understand the context of the Bank’s data and businesses to ensure properly developed models.
- 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 in to existing models to improve predictive results.
- Develop, maintain and manage satisfactory model documentation, including process narratives and performance monitoring guidelines to serve as reference source.
- Lead financial analysis and data support to other groups/departments across the Bank as required. Lead engagements with colleagues in Model Risk Management for model validation exercises.
- Provide guidance and direction to less experienced personnel regarding all aspects of data and financial analysis and development and management of predictive statistical models.
- 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 second line of defense and regulatory standards, policies and procedures.
- Serve as lead in managing Treasury projects and initiatives under guidance and direction of management. Present data, results and/or recommendations to senior management as necessary. May lead teams on either a project or full-time basis, providing performance feedback to management as appropriate.
- Understand and adhere to the Company’s risk and regulatory standards, policies and controls in accordance with the Company’s Risk Appetite. Identify risk-related issues needing escalation to management.
- Promote an environment that supports belonging and reflects the M&T Bank brand.
- 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.
Scope of Responsibilities:
The position serves as team lead in use of statistical programming languages to analyze Bank datasets and development, implementation and maintenance of behavioral models. It is important for the position to communicate with clear narratives, compelling data visualization and technical precision, both in-person and in writing, to enable audiences to understand analysis and forecasts. The position partners and collaborates with colleagues in related functions, including Credit Risk Management, Asset Liability and Liquidity Management, Model Risk Management and business lines to implement and understand models for Bank use. The position often leads team-based projects related to model development or implementation. This role is highly technical in nature and requires demonstrated attention to detail, execution and follow-up on multiple initiatives within Treasury and across the Bank. The ability to identify, analyze, rationalize and communicate complex business, data and statistical problems and recommend corresponding solutions while directing the work of others on the team is a key factor of success in this role. The position may supervise the work of interns and/or lead teams of up to three individual contributors, providing performance feedback to management as appropriate. The position also provides guidance and direction to less experienced personnel.
Education and Experience Required:
- 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
- Fluent in at least one open-source language for development: R, Python
- Experience in end-to-end model development lifecycle
- Experience working directly with model users and stakeholders who provide challenge and critical feedback
- Experience leading projects and initiatives involving other resources (team members)
- Minimum of 4 years’ on-the-job experience with pertinent statistical software packages (SAS, Python, Stata, R)
- 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
Education and Experience Preferred:
- 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
- Minimum of 5 years’ statistical analysis programming experience
- Commercial Real Estate credit model development experience ideal
- Financial Risk Manager (FRM) or Chartered Financial Analyst (CFA) designation
- Fluency and high proficiency in econometric/statistical techniques, especially time-series analysis, panel data methods and logistic regression
- Experience in balance sheet management and mathematical modeling of financial instruments offered by banks
- 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
- Proven leadership skills
- Strong desire to learn and contribute to a group
- Previous experience leading and directing the work of less experienced personnel
- Financial modeling experience (regulatory financial modeling or credit risk modeling is a plus)
- Experience in planning and managing project timelines and resources (experience in agile methods a plus)
- Exposure to SAS
- Experience with data management and principles (lineage, observability)
- Experience with git protocols, markdown tools, open-source package development, replicable coding environments is a plus