• Perform advanced credit risk data analysis in support of the consumer deposit overdrafts to identify areas of risk • Evaluate the risk of proposed product, process, policy, and model changes • Provide ongoing monitoring of credit metrics • Forecast overdraft charge-off losses for capital adequacy planning and the annual operating plan • Ensure compliance with internal governance processes and external regulatory requirements • Provide expertise and guidance to first-line partners • Support additional consumer unsecured products as needed • Assist in establishing, monitoring, evaluating and interpreting data with a risk management focus with an understanding of business strategy • Demonstrated working knowledge of relevant databases to provide data and credit risk analytical support to Senior Management • Perform data manipulation and analysis using SQL, SAS and Microsoft Excel and present results and recommendations to Credit Risk Management • Track portfolio performance and risk strategy results • Incorporate observations and data into existing models to improve predictive results • Identify deviations from forecast/expectations and explain variances • Identify risk and/or opportunities • Provide guidance and direction to lower level analysts regarding all aspects of data analysis and the construction of predictive statistical models • 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.

• Supports the development, optimization, and maintenance of data queries and analytics pipelines that power the Predictive Dairy Solutions platform. • Works within cross-functional teams consisting of business leaders, data scientists, dairy industry experts, and technology implementation specialists. • Responsible for writing, tuning, and managing SQL queries and data transformations that underpin dairy predictive algorithms and customer-facing analytics. • Must work with business stakeholders (internal and external). • Holds responsibility for ensuring data quality and compliance with established protocols for product implementation. • Requires strong capabilities in desktop manipulation of large, and sometimes complex, datasets. • Design, write, and optimize SQL queries that power dairy predictive algorithms and customer-facing platform analytics. • Support the deployment and lifecycle management of dairy analytics and predictive algorithms. • Perform data mapping and modeling exercises to translate business and scientific requirements. • Perform quality checks on available data to ensure completeness, internal consistency. • Collaborate with R&D and data science teams to support scientific innovation.

• Lead the strategy, roadmap, and execution for Data, Analytics, AI, Automation, and Innovation across the enterprise. • Establish enterprise data governance, architecture, engineering, analytics, and business intelligence capabilities that enable data-driven decision-making. • Lead AI strategy, governance, adoption, and value realization, including Generative AI, Agentic AI, Microsoft Copilot, Machine Learning, and Intelligent Automation. • Identify, prioritize, and deliver high-value AI and automation use cases that improve productivity, operational efficiency, customer experience, and business performance. • Evaluate and scale emerging technologies and innovation initiatives that support digital transformation and competitive advantage. • Partner with leaders across Architecture, Application Development, Infrastructure, IT Operations, and Cybersecurity to ensure alignment with enterprise technology strategies and modernization efforts. • Build and lead high-performing teams while fostering a culture of innovation, accountability, and continuous improvement. • Serve as a trusted advisor to executive leadership on technology-enabled business transformation and investment decisions. • Ensure compliance with security, privacy, governance, regulatory, and responsible AI requirements.

• Lead the design, development, testing, validation, and deployment of advanced analytics and machine learning solutions. • Manage analytics projects supporting fraud detection, fraud prevention, waste and abuse identification, and investigative activities. • Develop and evaluate predictive models, anomaly detection methods, risk models, and entity-resolution techniques. • Lead the development of analytics solutions using artificial intelligence, machine learning, natural language processing, graph analytics, and data visualization. • Identify and refine analytics use cases in collaboration with government stakeholders, investigators, and program teams. • Translate business, investigative, and operational requirements into technical analytics solutions. • Lead technical discovery, data assessment, feature development, model selection, and solution design activities. • Evaluate model performance, identify gaps, and recommend refinements or recalibration as needed. • Ensure analytics models and outputs are accurate, explainable, defensible, repeatable, and aligned with project objectives. • Track project progress, risks, issues, dependencies, and quality-control activities. • Conduct technical reviews and quality-control reviews of analytics work products before delivery. • Review code, models, documentation, data transformations, and analytic outputs for accuracy and completeness. • Support the deployment, monitoring, maintenance, and ongoing improvement of analytics models in production environments. • Collaborate with data engineers and data-management teams to support data ingestion, preparation, governance, quality, and lineage. • Develop and maintain technical documentation describing methodologies, data sources, model designs, assumptions, findings, and limitations. • Present analytical findings, technical recommendations, and project updates to technical and non-technical stakeholders. • Provide technical leadership, mentoring, and guidance to data scientists, analysts, and other project personnel.