Credit Review Data Steward Lead

M&T Bank•Buffalo, NY

About The Position

Leads the Credit Review Data and Analytics function within the Credit Review Professional Practices team, serving as the department’s expert in portfolio analytics, data architecture, reporting, sampling methodologies, and emerging AI capabilities. Owns the analytical framework supporting portfolio risk assessment, review universe development, Reviewable Entity definition, sample selection, portfolio monitoring, and executive reporting. Acts as a liaison between Credit Review and Enterprise Data, Technology, Risk, and Finance organizations. Ensures Credit Review data documentation and quality meet business needs in collaboration with domain and product stakeholders, and coordinates cross-domain efforts to identify, manage, and remediate data-quality issues. The role develops and maintains the Credit Review team’s review and Reviewable Entity universe; translates commercial and consumer loan portfolio data into Reviewable Entities; creates portfolio segmentation and risk-stratification methodologies; and supports portfolio risk assessment and review planning. It establishes repeatable data-extraction processes, validates data quality and completeness, maintains lineage documentation, and manages relationships with data owners and technology teams. The role designs statistically valid, risk-based, and judgmental sampling frameworks, and builds and oversees executive dashboards covering credit risk indicators, review findings, and review plan execution. The position also identifies and evaluates AI use cases, partners with Model Risk and Technology, develops AI deployment controls and governance, and pilots solutions for tasks such as credit memo analysis, review planning, sample selection, findings summarization, and risk-trend identification. Other related duties may be assigned. The position has 5–8 staff.

Requirements

Bachelor’s degree and a minimum of 7 years of related experience; or, in lieu of a degree, a combined minimum of 11 years of higher education and/or work experience, including a minimum of 7 years of related experience.
Minimum of 2 years’ managerial, supervisory, and/or work leadership experience.
Expert ability to pull data from Structured Query Language (SQL) platforms.
Proven ability to close data issues.
Experience owning business relationships, gathering complex business requirements, and translating them into technology requirements for engineers.
Experience setting user acceptance test parameters to ensure conformity to business objectives.
Knowledge of data management.

Nice To Haves

Credit Risk Management or Credit Review experience.
In-depth knowledge of data management, analysis, and warehousing products, systems, and workflows, such as Python, Power BI, and Alteryx.
Experience using data governance tools and familiarity with data warehousing concepts.
Experience leading remediation of complex data issues.
Strong understanding of statistical sampling methodologies and statistical analysis techniques and tools.
Strong understanding of AI, Generative AI, and Machine Learning applications and capabilities.
Excellent prompt engineering skills.
Familiarity with Application Programming Interface (API) integrations.
Portfolio analytics experience.

Responsibilities

Develop and maintain the Credit Review team’s review and Reviewable Entity universe.
Translate commercial and consumer loan portfolio data into Reviewable Entities.
Create methodologies for portfolio segmentation and risk stratification.
Develop and maintain portfolio monitoring metrics.
Support the portfolio Risk Assessment process and review planning.
Establish repeatable processes for extracting data from M&T platforms.
Validate data quality and completeness and maintain data lineage documentation.
Manage relationships with data owners and technology teams.
Design statistically valid sampling approaches, including risk-based and judgmental sampling frameworks.
Optimize sample sizes while maintaining appropriate coverage.
Support examiner and audit requests regarding sample methodology.
Build and oversee dashboards for delinquencies, defaults, charge-offs, Criticized/Classified assets, risk ratings, Watchlist credits, policy exceptions, review testing deviations and Findings, and review plan execution.
Identify AI use cases within Credit Review and evaluate Generative AI and predictive analytic solutions.
Partner with Model Risk and Technology and develop controls and governance around AI deployment.
Pilot AI solutions involving credit memo analysis, review planning, sample selection, Findings summarization, and risk-trend identification.
Coordinate cross-domain efforts to identify, manage, and remediate data-quality issues.
Perform other related duties as assigned.