• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains • Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems • Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics • Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes • Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact • Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics • Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes • Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes • Drive adoption and usage of shared data assets across business units • Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization) • Ensure solutions are aligned to real-world workflows and deliver tangible outcomes • Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships • Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases • Ensure alignment of definitions across domains to reduce fragmentation and duplication • Support both structured and unstructured data integration, enabling downstream AI and analytics applications • Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships • Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions • Own the end-to-end product lifecycle from discovery and definition to delivery and iteration • Define product requirements, success metrics, and release plans • Continuously refine the semantic data product based on user feedback and evolving business needs • Define critical metrics to measure adoption, usability, and impact of the semantic data product • Supervise usage and continuously improve accessibility and value delivery • Evangelize the value of connected semantic data across the organization • Partner with data governance teams to establish standards, definitions, and data quality expectations • Promote consistency, reusability, and scalability of data assets across the enterprise

• Deliver AI powered solutions driving both internal efficiencies and new customer value through data insights • AI/ML Solution Implementation: Work closely with business leaders and data scientists to understand user needs and transition AI/ML prototypes into production-ready solutions • Full-Scale Model Deployment: Design, develop, and deploy scalable machine learning models and algorithms to solve complex business problems. • Data Integration: Ensure efficient collection and integration of data from diverse sources for training and validation of AI models. • System Integration: Collaborate with tech teams to integrate AI/ML solutions seamlessly into existing systems and enhance data collection processes. • Performance Optimization: Continuously monitor and optimize model performance to ensure responsiveness and scalability. • Code Quality and Maintenance: Write clean, maintainable, and well-documented code, ensuring best practices in software development. • Process Improvement: Leverage AI/ML to identify opportunities for process improvement and contribute to strategic projects aimed at enhancing business performance and competitiveness. • Change Management and Adoption: Ensure successful adoption of AI/ML solutions by managing organizational change, assisting with user training, and conducting follow-up post implementation to support users and address any issues.

• Deliver AI powered solutions driving both internal efficiencies and new customer value through data insights • Work closely with business leaders and data scientists to understand user needs and transition AI/ML prototypes into production-ready solutions • Design, develop, and deploy scalable machine learning models and algorithms to solve complex business problems. • Ensure efficient collection and integration of data from diverse sources for training and validation of AI models. • Collaborate with tech teams to integrate AI/ML solutions seamlessly into existing systems and enhance data collection processes. • Continuously monitor and optimize model performance to ensure responsiveness and scalability. • Write clean, maintainable, and well-documented code, ensuring best practices in software development. • Leverage AI/ML to identify opportunities for process improvement and contribute to strategic projects aimed at enhancing business performance and competitiveness. • Ensure successful adoption of AI/ML solutions by managing organizational change, assisting with user training, and conducting follow-up post implementation to support users and address any issues.

• Deliver AI powered solutions driving both internal efficiencies and new customer value through data insights • Work closely with business leaders and data scientists to understand user needs and transition AI/ML prototypes into production-ready solutions • Design, develop, and deploy scalable machine learning models and algorithms to solve complex business problems • Ensure efficient collection and integration of data from diverse sources for training and validation of AI models • Collaborate with tech teams to integrate AI/ML solutions seamlessly into existing systems and enhance data collection processes • Continuously monitor and optimize model performance to ensure responsiveness and scalability • Write clean, maintainable, and well-documented code, ensuring best practices in software development • Leverage AI/ML to identify opportunities for process improvement and contribute to strategic projects aimed at enhancing business performance and competitiveness • Ensure successful adoption of AI/ML solutions by managing organizational change, assisting with user training, and conducting follow-up post implementation to support users and address any issues