Senior Manager, Data Science

Job Purpose: Lead the development, implementation, and governance of the Bank’s enterprise-wide Artificial Intelligence, Generative AI, Data Science, and advanced analytics capabilities. The role provides strategic and technical leadership across AI and Data Science platforms, data pipelines, predictive models, and enterprise use cases, translating business requirements into scalable data-driven solutions. The role also establishes appropriate AI governance and controls, drives enterprise adoption, and builds the Bank’s long-term internal AI, Data Science, and Data Engineering capabilities in collaboration with business, data, technology, and other relevant stakeholders. Job Accountabilities AI & Data Science Strategy and Capability Development • Lead the Bank’s enterprise AI, Generative AI, Data Science, and advanced analytics capabilities and operating model. • Drive the development and adoption of priority AI, machine learning, and advanced analytics use cases. • Provide strategic and technical leadership and establish standards and best practices across AI and Data Science. • Build and develop the Bank’s internal AI, Data Science, and Data Engineering capabilities. AI, Data & Analytics Platforms • Own the design, development, implementation, and governance of AI and analytics platforms. • Develop data pipelines and integrate multiple data sources to support AI and analytical solutions. • Support Microsoft Fabric and other relevant data and analytics platforms. • Oversee analytical solutions, dashboards, and insights to enable data-driven decision-making. Enterprise AI & Data Science Delivery • Translate business requirements into prioritized AI and Data Science initiatives. • Lead initiatives end-to-end from assessment and design through testing, deployment, and adoption. • Lead predictive analytics, machine learning, and advanced analytical solutions. • Provide technical oversight of AI solutions and ensure appropriate testing and integration. • Establish scalable and reusable solutions and standards to support enterprise-wide adoption. AI Governance, Risk & Controls • Lead the development and implementation of the Bank’s AI governance framework. • Establish AI policies, standards, controls, and requirements for assessment, approval, validation, and deployment. • Ensure AI and Data Science initiatives comply with regulatory, risk, data governance, and internal control requirements. • Oversee model performance, data quality, testing, validation, and lifecycle governance. Stakeholder & Cross-Functional Collaboration • Act as the Bank’s subject matter expert and central point of contact for AI and advanced Data Science initiatives. • Partner with business, Data, Innovation, Technology, Strategy, MIS, and other stakeholders to identify opportunities and drive adoption. • Provide senior management with updates on key initiatives, performance, risks, and priorities. • Manage relevant vendors and technology partners supporting AI and Data Science initiatives. Knowledge Transfer & Future Capability • Build internal AI and Data Science knowledge and promote knowledge sharing across the Bank. • Mentor and develop AI, Data Science, and Data Engineering resources as the function expands. • Drive knowledge transfer from external partners to strengthen internal capability and reduce vendor dependency.

Lead the development, implementation, and governance of the Bank’s enterprise-wide Artificial Intelligence, Generative AI, Data Science, and advanced analytics capabilities. Translate business requirements into scalable data-driven solutions and establish appropriate AI governance and controls. Provide strategic and technical leadership across AI and Data Science platforms, data pipelines, predictive models, and enterprise use cases. Own design, development, implementation, and governance of AI and analytics platforms; develop data pipelines; support Microsoft Fabric and other platforms; oversee dashboards and insights. Translate business requirements into prioritized AI and Data Science initiatives; lead end-to-end delivery from assessment to adoption; lead predictive analytics and machine learning solutions; provide technical oversight and ensure testing and integration; establish scalable, reusable solutions and standards for enterprise adoption. Lead AI governance, risk and controls, including policies, standards, controls, assessment, validation, and deployment; ensure compliance with regulatory, risk, data governance, and internal control requirements; oversee model performance, data quality, testing, validation, and lifecycle governance. Act as subject matter expert for AI and advanced Data Science initiatives; partner with multiple stakeholders to identify opportunities and drive adoption; provide senior management with updates on initiatives, performance, risks, and priorities; manage vendors and technology partners. Build internal AI and Data Science knowledge, mentor and develop resources, drive knowledge transfer from external partners to strengthen internal capability and reduce vendor dependency.