Senior Manager, Product Management – Field Intelligence

Position Summary

The 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.

Responsibilities

• Own the technology vision, product strategy, roadmap, architecture, and lifecycle for the enterprise Field Intelligence platform.
• Define and execute a multi-year roadmap that modernizes and consolidates field reporting, analytics, recommendation, and intelligence capabilities into a scalable enterprise platform.
• Define the target-state architecture across data, analytics, semantic, ontology, AI/agentic, integration, and user-experience layers.
• Balance delivery of new capabilities with platform modernization, legacy retirement, technical debt reduction, data quality, security, scalability, performance, and maintainability.
• Define product investment priorities based on business value, technical feasibility, architectural alignment, platform health, total cost of ownership, and measurable value realization.
• Evaluate build, buy, reuse, and partner options and maximize reuse of enterprise technology and data capabilities where appropriate.
• Own product lifecycle management from discovery and experimentation through production deployment, scaling, optimization, and retirement.
• Drive rationalization and retirement of legacy reporting and analytics capabilities as functionality migrates to the unified Field Intelligence platform.
• Define product success measures covering adoption, insight quality, recommendation quality, response time, reliability, user satisfaction, operational health, and business value.
• 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.
• Own the product requirements and governance for Analytics Ready Datasets (ARDs), metrics, semantic models, ontology, and metadata required to deliver consistent insights across channels.
• Partner with AI Engineering and technology partners to design reusable agentic capabilities, orchestration patterns, data access, monitoring, human oversight, and responsible AI controls.
• Lead development of new AI, generative AI, conversational analytics, recommendation, and agentic capabilities from concept through enterprise-scale production.
• Define non-functional requirements including response time, data freshness, concurrency, resiliency, observability, recoverability, security, and supportability.
• Manage Agile product backlogs and prioritize investments based on business value, product strategy, architecture, platform health, and operational needs.
• Manage product budgets, vendor spend, licensing, cloud/compute consumption, financial forecasts, and value realization.

Basic Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Data Science, Artificial Intelligence, or a closely related technical discipline.
• 8+ years of experience in Technology Product Management, Data & Analytics Products, Commercial Technology, AI-enabled products, Enterprise Applications, or related disciplines.
• Experience managing enterprise technology products from strategy and architecture through delivery, operations, adoption, and lifecycle management.
• Experience with enterprise analytics platforms, cloud data platforms, data products, semantic models, APIs, and modern data architectures.
• Technical experience designing or building foundational data layers, dimensional or analytical data models, analytics-ready datasets / data products, metrics layers, and semantic models.
• Experience with data engineering, analytics engineering, or similar technical development work using modern cloud data platforms and data transformation / modeling approaches.
• Experience with AI / machine learning and familiarity with the data science lifecycle.
• Experience prototyping, building, or materially contributing to at least one AI-enabled product or capability.
• Experience partnering with Enterprise Architecture, Data Engineering, Data Architecture, AI Engineering, Cloud Engineering, Information Security, and platform teams.
• Experience defining technology strategies, product roadmaps, platform architectures, integrations, and non-functional requirements.
• Experience managing technology performance, scalability, reliability, and cost optimization across data and analytics products.

Preferred Qualifications

• Familiarity with artificial intelligence, machine learning, generative AI, large language models, conversational AI, recommendation engines, and agentic AI.
• Demonstrated ability to lead complex cross-functional initiatives and influence stakeholders across a large organization.
• Excellent communication and presentation skills.
• Experience applying Agile Product Management methodologies.
• Experience within the pharmaceutical, biotechnology, healthcare, or life sciences industry.
• Experience implementing enterprise field reporting, conversational analytics, Opportunity Engine / Next Best Action, or AI-powered commercial capabilities.
• Experience with ThoughtSpot, Tableau, Databricks, Snowflake, Veeva Vault CRM, or comparable analytics, data, and CRM technologies.
• Experience defining semantic layers, ontologies, KPI standards, reusable data products, or enterprise analytics architectures.
• Working proficiency with relevant data and analytics technologies (for example SQL, Python, notebooks, data transformation frameworks).
• Experience designing or managing AI agents, recommendation systems, natural-language analytics, or generative AI products.
• Experience leading reporting platform modernization, rationalization, and legacy retirement initiatives.
• Experience evaluating AI, analytics, and data vendors and strategic technology partnerships.
• Familiarity with responsible AI frameworks, model governance, data governance, and AI lifecycle management.
• Relevant cloud, data, AI, Agile, or product management certifications.
• Master's degree in Computer Science, Artificial Intelligence, Information Systems, Engineering, Analytics, Business, or a related discipline.