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 for the US Commercial organization. The role serves as Technology Product Manager for the end-to-end Field Intelligence ecosystem, turning trusted commercial data into actionable insights, recommendations, and AI-powered experiences that support field effectiveness and decision-making. The product scope includes reporting and dashboards, conversational analytics, proactive insight delivery, pre-call planning, incentive compensation, Opportunity Engine / Next Best Action, and emerging AI, generative AI, and agentic capabilities. The role is accountable across the data-to-experience stack, including foundational data layers, analytics-ready data products, metrics, semantic and ontology layers, AI and agentic services, and user experiences on web, iPad, and Vault CRM. It partners with data, architecture, AI, analytics, cloud, platform, CRM, commercial, and external technology teams, and integrates intelligence from enterprise and global AI capabilities while maintaining clear ownership boundaries. The position sets product direction and architecture, leads capability development and modernization, and balances new features with platform health, security, scalability, reliability, cost, and legacy retirement. It defines product requirements and governance for data products and semantic assets, establishes measurable success criteria, and ensures consistent metrics and business rules across channels. It also manages integrations, vendors, delivery, budgets, operational readiness, and adoption in partnership with business and technology stakeholders.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Data Science, Artificial Intelligence, or a closely related technical discipline.
At least 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/machine learning and familiarity with the data science lifecycle, including data or feature 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 in 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 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, recommendations, 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.
Set 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, maximizing reuse of enterprise technology and data capabilities where appropriate.
Manage product capabilities including conversational analytics, proactive insights, reporting and dashboards, self-service analytics, pre-call planning, incentive compensation insights, Opportunity Engine / Next Best Action, AI-powered insight delivery, and future AI capabilities.
Own product lifecycle management from discovery and experimentation through production deployment, scaling, optimization, and retirement.
Define principles for selecting dashboards, conversational analytics, proactive insights, recommendations, or embedded experiences to deliver information.
Rationalize and retire legacy reporting and analytics as functionality moves 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 product requirements and governance for analytics-ready datasets, metrics, semantic models, ontology, and metadata.
Ensure consistent KPI definitions, business rules, calculation logic, terminology, and reusable semantic assets across the platform.
Partner with data and AI teams to design reusable agentic capabilities, orchestration, data access, monitoring, human oversight, and responsible AI controls.
Define integration patterns for using enterprise and global AI-generated intelligence in Field Intelligence experiences without duplicating ownership of source capabilities.
Lead AI, generative AI, conversational analytics, recommendation, and agentic capabilities from concept through enterprise-scale production.
Assess AI models and technology choices for business fit, quality, explainability, security, latency, scalability, interoperability, and cost.
Create reusable technology patterns for future AI use cases with appropriate 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.
Monitor query performance, conversational response times, data latency, recommendation processing, platform availability, usage, and operational incidents.
Optimize compute consumption, vendor usage, infrastructure utilization, and total cost of ownership while balancing user experience and technology cost.
Lead root-cause analysis and permanent remediation for recurring platform, data, model, integration, or performance issues.
Own the technology product experience 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 capabilities into field workflows while minimizing context switching and application complexity.
Partner with the Customer Planning & AI Enablement Product Manager to ingest and surface enterprise/global AI outputs in Field Intelligence.
Ensure 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 stakeholders.
Translate business priorities and field-user needs into scalable products, architecture decisions, and investment roadmaps.
Challenge requests that duplicate existing capabilities, create inconsistent data logic, or introduce unsustainable technology complexity.
Communicate 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, deployments, production readiness, and transition to operational support.
Manage Agile product backlogs and prioritize investment 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.
Convert experimental capabilities that demonstrate value into scalable, supportable, governed enterprise products.