Senior Manager, Product Management - Field Intelligence

Business Introduction GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver 40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centered on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems.Our diverse portfolio consists of vaccines, specialty medicines, and general medicines.Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.Position SummaryThe Senior Manager, Product Management - Field Intelligence is responsible for the technology strategy, roadmap, architecture, delivery, lifecycle management, and operational excellence of GSK's unified Field Intelligence platform supporting the US Commercial organization. This role serves as the Technology Product Manager for the end-to-end Field Intelligence ecosystem, transforming trusted commercial data into actionable insights, recommendations, and AI-powered experiences that improve field effectiveness and decision-making. The product scope spans traditional reporting and dashboards, conversational analytics, proactive insight delivery, pre-call planning, incentive compensation, Opportunity Engine / Next Best Action, and new AI, generative AI, and agentic capabilities developed for the field. The Senior Manager is accountable for the technology product across the full data-to-experience stack, partnering with Data Engineering, Data Architecture, Enterprise Architecture, AI Engineering, Analytics, Cloud and Platform Engineering, CRM Engineering, Commercial stakeholders, and external technology partners. This includes foundational data layers, analytics-ready data products, metrics, semantic and ontology layers, AI/agentic services, and user experiences delivered through web, iPad, and Vault CRM. The role will also integrate intelligence generated by enterprise and global AI capabilities owned or enabled elsewhere in GSK into the Field Intelligence platform, creating a cohesive experience for field users while maintaining clear accountability boundaries with those source capabilities.ResponsibilitiesThis role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following: Technology Product Strategy & Roadmap 1. Own the technology vision, product strategy, roadmap, architecture, and lifecycle for the enterprise Field Intelligence platform. 2. Define and execute a multi-year roadmap that modernizes and consolidates field reporting, analytics, recommendation, and intelligence capabilities into a scalable enterprise platform. 3. Define the target-state architecture across data, analytics, semantic, ontology, AI/agentic, integration, and user-experience layers. 4. Balance delivery of new capabilities with platform modernization, legacy retirement, technical debt reduction, data quality, security, scalability, performance, and maintainability. 5. Define product investment priorities based on business value, technical feasibility, architectural alignment, platform health, total cost of ownership, and measurable value realization. 6. Evaluate build, buy, reuse, and partner options and maximize reuse of enterprise technology and data capabilities where appropriate. Field Intelligence Product Management - Conversational analytics through chat and voice - Proactive insights and push notifications - Enterprise reporting, dashboards, graphs, charts, and scorecards - Self-service analytics - Pre-call planning for HCPs and accounts - Incentive Compensation insights and reporting - Opportunity Engine / Next Best Action - AI-powered insight del very - New AI agents, generative AI, agentic AI, and future field intelligence capabilities 1. Own product lifecycle management from discovery and experimentation through production deployment, scaling, optimization, and retirement. 2. Define clear product principles for when information should be delivered through dashboards, conversational analytics, proactive insights, recommendations, or embedded experiences. 3. Drive rationalization and retirement of legacy reporting and analytics capabilities as functionality migrates to the unified Field Intelligence platform. 4. Define product success measures covering adoption, insight quality, recommendation quality, response time, reliability, user satisfaction, operational health, and business value. Data, Semantic & AI Platform Architecture 1. Own end-to-end product accountability across the Field Intelligence technology stack, partnering with technical teams responsible for implementation and operation of the underlying components. 2. Partner with Data Engineering and Data Architecture teams on foundational Bronze, Silver, and Gold data layers and the data products required by Field Intelligence capabilities. 3. Own the product requirements and governance for Analytics Ready Datasets (ARDs), metrics, semantic models, ontology, and metadata required to deliver consistent insights across channels. 4. Ensure standardized KPI definitions, business rules, calculation logic, terminology, and reusable semantic assets are applied consistently across dashboards, conversational analytics, recommendations, and AI-powered experiences. 5. Partner with AI Engineering and technology partners to design reusable agentic capabilities, orchestration patterns, data access, monitoring, human oversight, and responsible AI controls. 6. Define integration patterns that allow intelligence generated by enterprise/global AI capabilities to be consumed within Field Intelligence experiences without duplicating source capability ownership. AI Product Development & Innovation 1. Lead development of new AI, generative AI, conversational analytics, recommendation, and agentic capabilities from concept through enterprise-scale production. 2. Partner with AI Engineering, Data Science, Enterprise Architecture, Security, and external technology partners to translate emerging AI technologies into scalable field products. 3. Lead build, buy, reuse, and partner evaluations for new Field Intelligence technologies and vendors. 4. Assess model and technology choices based on business fit, quality, explainability, security, latency, scalability, interoperability, and cost. 5. Prevent unnecessary duplication by comparing proposed capabilities with existing enterprise/global AI investments and defining appropriate integration or reuse strategies. 6. Create reusable technology patterns that accelerate future AI use cases while maintaining appropriate governance and operational controls. Platform Performance, Sca