Senior Ai Product Manager

GSK•Philadelphia, PA

7d•Hybrid

About The Position

GSK is seeking a senior product leader to shape enterprise data and AI platforms. The role sets multi-year product direction, develops platform strategy with the Director and Principal Engineers, leads a team of Product Managers, and represents product at Director- and VP-level. It operates at the intersection of enterprise data and AI, supporting innovation, decision-making, and business transformation. The position owns a portfolio spanning data platform products—including lakehouse capabilities, pipelines, governance, and BI—and AI/ML products such as model lifecycle tooling, RAG, generative AI, and multi-agent systems. It also leads portfolio prioritization, executive stakeholder engagement, AI and data product strategy, delivery practices, team development, and platform KPI reporting. This is a hybrid position in the United States, with on-site work part of the week and remote work the remainder. Strong collaboration across teams and time zones is required.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Business Analytics, Information Systems, or a related field.
At least 8 years of product management experience, including at least 4 years working on data platforms, AI/ML products, or enterprise analytics infrastructure.
Experience owning a multi-product portfolio and leading other Product Managers or Business Analysts.
Strong product judgment, portfolio-level prioritization, high-stakes trade-off decision-making, and OKR-driven roadmapping across multiple engineering squads.
Mastery of agile delivery and tooling such as Jira, Confluence, Productboard, and Miro; ability to produce strategy documents, business cases, roadmaps, and PRDs.
Experience with lakehouse architecture, data mesh, batch and streaming pipelines, data cataloguing, lineage, data quality, and self-service analytics.
Experience with Azure Synapse, Databricks, Delta Lake, and dbt, including technical architecture discussions with Principal Engineers.
Experience with BI tooling such as Tableau and Power BI, semantic layers, and the ability to read and reason about SQL and Python.
Experience with the end-to-end ML lifecycle, MLOps, LLMOps, RAG architectures, vector databases, and multi-agent systems.
Experience managing production generative AI or LLM-powered products, including prompt engineering, model evaluation, hallucination mitigation, and Responsible AI integration.
Experience communicating with executives, including preparing board-ready decks, investment proposals, and narratives for technical and business audiences.
Experience influencing Director- and VP-level stakeholders and working across distributed, matrixed organizations.

Nice To Haves

Advanced AI product experience, including fine-tuning pipelines, synthetic data, or RLHF-powered products.
Data mesh experience, including domain data product ownership, data contracts, or federated governance.
Regulatory experience with GxP, the EU AI Act, FDA AI guidance, GDPR, or the DPDP Act in India.
Pharmaceutical, life sciences, or regulated healthcare industry background.
Involvement in open-source AI/ML or data communities.
Formal Product Management certification such as CSPO, AIPMM, SAFe, or equivalent.
Experience with SAP landscapes, including SAP Business Data Cloud and SAP data sources feeding enterprise data platforms.
Exposure to ECC-to-S/4HANA transformation programs, ideally with AI or generative AI used to augment migration, data readiness, or process transformation.

Responsibilities

Own and drive a multi-year product vision and roadmap for data platform and AI/ML products.
Align product strategy with enterprise data architecture and GSK business priorities in partnership with the Director, Enterprise AI Platform and Principal Engineers.
Prioritize work across concurrent initiatives, balancing new capabilities, technical debt, and regulatory compliance.
Assess emerging technologies, including data mesh, generative AI, advanced RAG, and multi-agent orchestration, to inform platform investments.
Engage Director- and VP-level stakeholders across R&D, Supply Chain, Commercial, and IT to shape demand, align priorities, and communicate platform value and ROI.
Lead executive roadmap reviews, investment proposals, and architecture governance forums on behalf of the product organization.
Build strategic relationships with data domain leads, AI engineering leaders, and data scientists to understand long-term needs.
Own the AI/ML product lifecycle, including model development tooling, MLOps and LLMOps pipelines, generative AI applications, RAG systems, and multi-agent orchestration frameworks.
Define AI-ready data product requirements, including embeddings, feature stores, versioned training data, and evaluation frameworks.
Champion Responsible AI practices, including ethical model development, bias mitigation, explainability, and compliance with relevant guidance.
Own data platform products such as ingestion pipelines, lakehouse layers, cataloguing and lineage, data quality, and self-service analytics foundations.
Lead analytics and BI product development, including semantic layers, metrics catalogues, and data marts.
Drive data governance product strategy covering metadata management, data classification, access control, and compliance tooling.
Lead, mentor, and develop Product Managers and Business Analysts in the platform product organization.
Maintain backlog health across engineering squads; write epics, user stories, and acceptance criteria; and drive agile ceremonies with engineering leadership.
Define and report platform KPIs for adoption, AI quality, pipeline reliability, time-to-insight, delivery velocity, and stakeholder satisfaction.