About the Role
The position involves leading the company’s enterprise AI, Generative AI, Data Science, and advanced analytics capabilities and operating model. The role is pivotal in driving the development and adoption of priority AI, machine learning, and advanced analytics use cases while providing strategic and technical leadership in establishing standards and best practices across AI and Data Science. Furthermore, the individual will be responsible for building and developing the company’s internal AI, Data Science, and Data Engineering capabilities.
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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 company’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 company’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 company.
- 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.
