Senior Manager, Product Management – Field Intelligence

GSK•Philadelphia, PA

About The Position

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. The role serves as Technology Product Manager for the end-to-end Field Intelligence ecosystem, transforming commercial data into actionable insights, recommendations, and AI-powered experiences that improve field effectiveness and decision-making. The product scope includes enterprise reporting and dashboards, conversational analytics, proactive insights, self-service analytics, pre-call planning, incentive compensation insights, Opportunity Engine / Next Best Action, and new AI, generative AI, and agentic capabilities. The role has product accountability across the data-to-experience stack, including foundational data layers, analytics-ready data products, metrics, semantic and ontology layers, AI and agentic services, integrations, and web, iPad, and Vault CRM experiences. It partners with technical, commercial, and external teams and integrates intelligence from enterprise and global AI capabilities while maintaining clear ownership boundaries. The Senior Manager defines and executes the platform strategy and multi-year roadmap; owns product lifecycle management and success measures; guides data, semantic, and AI platform requirements; and leads the development and scaling of AI-enabled products. The role also owns platform performance, reliability, scalability, and cost optimization; drives intuitive field experiences and integrations; builds partnerships across business and technology organizations; and manages vendors, delivery, budgets, and operational readiness.

Requirements

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 or data products, metrics layers, and semantic models; able to challenge designs and contribute directly when needed.
Experience with data engineering, analytics engineering, or similar technical development using modern cloud data platforms and data transformation or modeling approaches.
Experience with AI or machine learning and familiarity with the data science lifecycle, including 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.

Nice To Haves

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 explaining complex technology concepts and presenting 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 such as SQL, Python, notebooks, or data transformation frameworks, sufficient to inspect data, validate models, prototype concepts, and engage deeply with engineering and data science teams.
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.

Responsibilities

Own the technology vision, product strategy, roadmap, architecture, and lifecycle for the enterprise Field Intelligence platform.
Define and execute a multi-year roadmap to modernize and consolidate field reporting, analytics, recommendation, and intelligence capabilities.
Define target-state architecture across data, analytics, semantic, ontology, AI/agentic, integration, and user-experience layers.
Balance new capability delivery with platform modernization, legacy retirement, technical debt reduction, data quality, security, scalability, performance, and maintainability.
Prioritize product investments 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, maximizing reuse of enterprise technology and data capabilities where appropriate.
Own Field Intelligence products including conversational analytics, proactive insights, reporting and dashboards, self-service analytics, pre-call planning, incentive compensation insights, Opportunity Engine / Next Best Action, and AI-powered experiences.
Manage product lifecycle from discovery and experimentation through production deployment, scaling, optimization, and retirement.
Define principles for delivering information through dashboards, conversational analytics, proactive insights, recommendations, and embedded experiences.
Rationalize and retire legacy reporting and analytics as capabilities migrate to the unified platform.
Define product success measures for adoption, insight and recommendation quality, response time, reliability, user satisfaction, operational health, and business value.
Own end-to-end product accountability across the Field Intelligence technology stack, partnering with technical teams responsible for implementation and operation.
Partner with Data Engineering and Data Architecture on foundational data layers and the data products required by Field Intelligence capabilities.
Own product requirements and governance for Analytics Ready Datasets, metrics, semantic models, ontology, and metadata.
Promote consistent KPI definitions, business rules, calculation logic, terminology, and reusable semantic assets across product 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 for incorporating enterprise and global AI intelligence into Field Intelligence experiences without duplicating source capability ownership.
Lead 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 AI technologies into scalable field products.
Assess models and technologies for business fit, quality, explainability, security, latency, scalability, interoperability, and cost; avoid unnecessary duplication of existing investments.
Create reusable technology patterns that accelerate future AI use cases while preserving governance and operational controls.
Own performance, scalability, reliability, availability, maintainability, and operational health of Field Intelligence experiences.
Define non-functional requirements, including response time, data freshness, concurrency, resiliency, observability, recoverability, security, and supportability.
Work with engineering and architecture teams to ensure infrastructure, data models, caching, query patterns, and compute architecture support strong field-user performance.
Optimize the balance between user experience and technology cost, including 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.
Lead root-cause analysis and permanent remediation of recurring platform, data, model, integration, or performance issues.
Own technology product experiences across web, iPad, Vault CRM, chat, voice, email, and notification channels.
Partner with UX specialists and field users to design intuitive experiences that move users from information to insight to action.
Integrate Field Intelligence into field workflows while minimizing context switching and application complexity.
Partner with the Customer Planning & AI Enablement Product Manager to ingest and surface outputs from enterprise and global AI capabilities.
Ensure connected-product integrations, APIs, data contracts, and service-level expectations are scalable, reliable, secure, monitored, and reusable.
Build 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 products, architecture decisions, and investment roadmaps.
Challenge requests that duplicate existing capabilities, introduce inconsistent data logic, or create 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.
Manage strategic relationships with analytics, AI, data, platform, and implementation partners.
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 and compute consumption, financial forecasts, and value realization.
Ensure experimental capabilities that demonstrate value are developed into scalable, supportable, governed enterprise products.