Associate Director Product Owner, Medical Data Products

Overview

Own the data products and enterprise AI Context Layer for GSK Medical, spanning Data Generation, Scientific Communications, Medical Information, Publications and Channel Engagement, as a senior data product leader.

What you'll do

• Own the strategy, roadmap and value-based backlog for the Medical AI Context Layer and the data generation, content and channel data products that feed it.
• Define and deliver a governed semantic layer (shared medical data ontology, business glossary, knowledge graph and metadata) linking medical objectives, tactics, evidence, content, engagement and outcomes.
• Drive adoption of medical data products and the AI Context Layer to reduce time-to-insight for Global Medical Teams and Local Operating Companies, measure ROI of content and engagement, and increase reuse of trusted context across GenAI and agentic use cases.
• Act as the voice of GMTs and LOCs through continuous user research, turning pain points into an actionable, user-centric backlog.
• Set success metrics up front, prove value through proofs of value before scaling, and track KPIs such as adoption, data quality and user satisfaction.
• Lead change management, demos and training to drive enterprise-wide adoption and data literacy.
• Oversee end-to-end data ingestion and the target canonical/conformed data model connecting engagement data to GSK Medical data, balancing technical debt against feature delivery.
• Oversee the data lake governance framework (access models, schemas, classification, lineage, metadata, quality and storage standards aligned to CODE Orange), partnering with platform, security and data-owner teams and building GxP and data protection in from the start.
• Align platforms, engineering teams and vendors around shared outcomes and manage dependencies through to completion.
• Use approved AI tools to accelerate discovery and backlog trade-offs, keeping AI in support of decisions.
• Ensure data assets are structured, scalable and of sufficient quality to power GenAI, predictive analytics and agentic workflows.

What you'll need

• Bachelor's degree or equivalent experience in Computer Science, Data Engineering, Information Systems, Mathematics or a related discipline.
• 8+ years of experience in data architecture, data engineering, data product ownership or related roles, including delivering measurable business outcomes.
• Experience designing enterprise-scale data lake/lakehouse architectures on Azure or other cloud data platforms, including owning the target architecture.
• Experience in conceptual, logical and physical data modelling, including translating varied source data into canonical (standardized) data models.
• Experience with SQL, Databricks and/or Snowflake, and understanding of both relational and non-relational data structures.
• Experience using data governance and semantic architecture approaches to enable enterprise-grade data governance and semantic layers, including Databricks Unity Catalog and Genie Ontology.
• Experience with AI engineering and agent orchestration to enable secure AI agents and retrieval systems utilizing Databricks AI Search, the Model Context Protocol (MCP) and the Databricks Agent Framework.
• Experience designing data ingestion, transformation, integration and orchestration patterns.
• Experience with data governance, metadata, lineage, data quality and security in a regulated environment, such as role-based access, data privacy and GxP, using GSK CODE Orange or equivalent enterprise data platform standards.
• Experience working within enterprise architecture and technology standards.
• Experience in user research, defining product KPIs and driving adoption through demos and training, translating user needs into a prioritized, value-based backlog.
• Experience communicating data architecture to technical and non-technical stakeholders and aligning partners across functions in a matrixed organization.
• Experience leading cross-functional teams, partners and vendors without direct line management.

Nice to have

• Experience with Azure Databricks, Spark and Delta Lake.
• Experience with Microsoft Fabric, Synapse or Azure Data Lake Storage (ADLS), and Microsoft Purview or equivalent data catalogue tools.
• Experience with canonical data models and API-based data integration.
• Experience with data mesh and data product operating models, knowledge graphs and ontologies.
• Experience with GenAI, retrieval-augmented generation (RAG), vector databases and ML feature engineering.
• Experience in pharmaceutical, healthcare or other regulated environments, including handling sensitive and personal data; Medical Affairs engagement data and omnichannel measurement experience is an advantage.
• Experience with Agile ways of working, including proofs of value and retrospectives.

Details

• Location: Bengaluru Luxor North Tower.