Credit Model Development Data Architect Expert

M&T Bank•Buffalo, NY

6d•Hybrid

About The Position

Develops and builds complex systems of models to analyze diverse big data sources to generate insights and solutions for business partners and product enhancement. Leads work to develop, test and validate models that drive business value. Identifies, interprets, and communicates insights from data to management. Mentors less experienced team members.

Requirements

Bachelor’s degree and a minimum of 6 years’ proven quantitative behavioral modeling 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
Minimum of 6 years’ on-the-job experience with pertinent statistical software packages (SAS, Python, Stata, R)
Minimum of 6 years’ on-the-job experience with data management environment, such as SQL Server Management Studio
Minimum of 6 years’ on-the-job experience analyzing large data sets and explaining results of analysis through concise written and verbal communication as well as charts/graphs
Experience with various hybrid databases both on premise and in the cloud
Experience analyzing and explaining results of large data sets analyses

Nice To Haves

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 8 years’ statistical analysis programming experience
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

Responsibilities

Work to solve the most challenging data related problems using different analytical and statistical approaches across the organization
Mentor less experienced data scientists on current industry best practices for preventing data drift and sourcing, ingesting, and cleaning data.
Build complex econometric, statistical and machine learning models for various problems inclusive of classification, clustering, pattern analysis, sampling, and simulations.
Commit complex code into model repository to serve as a source for other team members and provide feedback on other code in the repository.
Lead the development of complex champion/challenger models and adjust models accordingly.
Develop and implement framework for building self-healing models.
Select and refine complex models, taking into account performance, reliability and stability metrics as well as business feedback.
Leverage model outputs to identify actionable insights, suggest recommendations and influence the direction of cross-functional groups by effectively communicating model outcomes, impacts and business value.
Guide and train less experienced data scientists on model development, selection, refinement, measurements and visualizations.
Own relationship with data clients to understand their unique research questions and lead development of new research approaches including adaption of research techniques based on client needs.
Lead code review to ensure code is efficient, accurate, and leveraging industry best practices.
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.
Design, implement, maintain and enhance internal controls to mitigate risk on an ongoing basis.
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.