Position Summary
The Senior Manager, Product Management 6 Field Intelligence is responsible for the technology strategy, roadmap, architecture, delivery, lifecycle management, and operational excellence of GSK 27s 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.
Responsibilities
This 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
- 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.
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 delivery
- New AI agents, generative AI, agentic AI, and future field intelligence capabilities
- Own product lifecycle management from discovery and experimentation through production deployment, scaling, optimization, and retirement.
- Define clear product principles for when information should be delivered through dashboards, conversational analytics, proactive insights, recommendations, or embedded experiences.
- 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.
Data, Semantic & AI Platform Architecture
- 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.
- 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.
- 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.
- Partner with AI Engineering and technology partners to design reusable agentic capabilities, orchestration patterns, data access, monitoring, human oversight, and responsible AI controls.
- 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
- Lead development of new AI, generative AI, conversational analytics, recommendation, and agentic capabilities from concept through enterprise-scale production.
- Partner with AI Engineering, Data Science, Enterprise Architecture, Security, and external technology partners to translate emerging AI technologies into scalable field products.
- Lead build, buy, reuse, and partner evaluations for new Field Intelligence technologies and vendors.
- Assess model and technology choices based on business fit, quality, explainability, security, latency, scalability, interoperability, and cost.
- Prevent unnecessary duplication by comparing proposed capabilities with existing enterprise/global AI investments and defining appropriate integration or reuse strategies.
- Create reusable technology patterns that accelerate future AI use cases while maintaining appropriate governance and operational controls.
Platform Performance, Scalability & Cost Optimization
- Own the performance, scalability, reliability, availability, maintainability, and operational health of Field Intelligence user experiences, including dashboards and conversational analytics.
- Define non-functional requirements including response time, data freshness, concurrency, resiliency, observability, recoverability, security, and supportability.
- Partner with Data Engineering, Cloud Engineering, Technical Architecture, Data Architecture, AI Engineering, and platform experts to ensure the correct infrastructure, data models, caching, query patterns, and compute architecture are in place to deliver strong field-user performance.
- Actively manage the trade-off between user experience and technology cost, optimizing compute consumption, vendor usage, infrastructure utilization, and total cost of ownership.
- Monitor query performance, conversational response times, data latency, recommendation processing, platform availability, usage patterns, and operational incidents.
- Drive root-cause analysis and permanent remediation of recurring platform, data, model, integration, or performance issues.
User Experience, Integration & Field Delivery
- Own the technology product experience across web, iPad, Vault CRM embedded experiences, chat, voice, email, and notification channels.
- Partner with UX specialists and field users to design intuitive experiences that move users efficiently from information to insight to action.
- Integrate Field Intelligence capabilities into field workflows while minimizing context switching and unnecessary application complexity.
- Partner with the Customer Planning & AI Enablement Product Manager to ingest and surface outputs from enterprise/global AI capabilities within the Field Intelligence platform.
- Ensure integrations, APIs, data contracts, and service-level expectations across connected products are scalable, reliable, secure, monitored, and reusable.
Business Partnership & Technology Leadership
- Build strong partnerships with Sales, Commercial Operations, Insights & Analytics, Marketing, Medical, Finance, Incentive Compensation, Digital, Data & Technology, and other stakeholder organizations.
- Translate business priorities and field-user needs into scalable technology products, architecture decisions, and investment roadmaps.
- Challenge requests that duplicate existing capabilities, create inconsistent data logic, or introduce unsustainable technology complexity.
- Communicate product strategy, architecture, solution options, performance, risks, dependencies, and investment recommendations to senior business and technology leaders.
- Partner with Change Management, Training, and Communications teams to support adoption and organizational readiness.
Vendor, Delivery & Financial Management
- Manage strategic relationships with analytics, AI, data, platform, and implementation partners supporting the Field Intelligence ecosystem.
- Lead product releases, pilots, testing strategies, deployments, production readiness, and transition to operational support.
- 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.
- Ensure experimental capabilities that demonstrate value are converted into scalable, supportable, governed enterprise products.
Why You?
Basic Qualification
We are seeking professionals with the following required skills and qualifications to help us achieve our goals
- Bachelor 27s 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; able to work at sufficient technical depth to challenge designs and contribute directly when needed.
- 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, including feature or data preparation, model development concepts, evaluation, deployment considerations, and monitoring.
- Experience prototyping, building, or materially contributing to at least one AI-enabled product or capability, such as a recommendation engine, conversational analytics solution, generative AI application, AI agent, or machine-learning-powered product.
- 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 Qualification
If you have the following characteristics, it would be a plus
- 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, including the ability to explain complex technology concepts and present solutions, trade-offs, risks, and recommendations to senior leaders.
- 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, or comparable tools) sufficient to inspect data, validate models, prototype concepts, and engage deeply with engineerin
