Commercial Data Ecosystems and AI readiness Lead

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Job Description

ABOUT THE ROLE: 

The Commercial Data Ecosystems & AI Readiness Lead is responsible for building and governing scalable, trusted, and AI-ready commercial data foundations across CEE GTM platforms and capabilities.

The role drives alignment of commercial data, metadata, governance, and semantic standards across business units, platforms, and regions to enable analytics, GenAI, and future Agentic AI use cases.

Working across Commercial, Enterprise Data & AI, AI/D, and platform teams, the role ensures commercial data ecosystems are interoperable, reusable, governed, and aligned with enterprise standards.

Key responsibilities include:

• Defining commercial data governance and AI-readiness standards 

• Driving metadata, lineage, data quality, and semantic consistency across platforms 

• Enabling reusable and scalable commercial data products and services 

• Supporting AI/GenAI use cases through trusted and contextualized data foundations 

• Aligning commercial platforms, business units, and enterprise data capabilities 

• Partnering with business and technology stakeholders to drive adoption and transformation 

ACCOUNTABILITIES: 

Enterprise Commercial Data Ecosystems & AI Readiness

• Define and operationalize the enterprise strategy for commercial data ecosystems, semantic interoperability, and AI-ready architectures across GTM platforms 

• Establish scalable governance and operating models for commercial metadata, ontology alignment, lineage, interoperability, and reusable data assets 

• Drive alignment between commercial business priorities, enterprise data strategy, and AI transformation initiatives 

• Enable AI-ready data ecosystems supporting analytics, GenAI, RAG, knowledge graph, and Agentic AI capabilities 

• Partner with enterprise data and AI organizations to ensure commercial data ecosystems align with enterprise AI, security, privacy, and governance standards

Commercial Metadata, Governance & Semantic Foundations

• Establish and govern enterprise-wide metadata standards, business glossaries, semantic definitions, lineage frameworks, and Critical Data Elements (CDEs) 

• Define governance frameworks for: 

• Business metadata 

• Technical metadata 

• Data quality 

• Access controls 

• Semantic consistency 

• Lifecycle management 

• Lead the adoption of ontology-aligned and semantically interoperable data structures across commercial domains 

• Govern and approve changes to commercial data definitions, lineage, semantic structures, and interoperability models across business units and geographies 

• Ensure enterprise-critical commercial data assets are traceable to business processes, decision-making, analytics, and AI consumption layers 

AI & GenAI Data Enablement

• Enable trusted and contextualized data ecosystems supporting GenAI, RAG, intelligent search, semantic reasoning, and AI agent use cases 

• Partner with AI and architecture teams to establish AI-ready commercial data foundations and semantic enrichment capabilities 

• Support the operationalization of reusable commercial knowledge assets, semantic layers, and contextual data services for AI-driven capabilities 

• Ensure commercial data ecosystems are optimized for future AI and Agentic AIscalability

Stakeholder Engagement 

• Act as a strategic advisor across commercial, data, AI, and platform organizations on commercial data and AI-readiness initiatives 

• Build and maintain governance forums and communication channels across enterprise stakeholders 

• Provide leadership visibility on progress, risks, priorities, and transformation outcomes 

• Influence enterprise adoption of standards and governance practices through strong business engagement, storytelling, and technical credibility 

• Drive alignment between enterprise transformation priorities and commercial data ecosystem capabilities

EDUCATION, BEHAVIOURAL COMPETENCIES AND SKILLS:(List the essential and desirable education and competency requirements to perform the primary responsibilities of the job.  Any minimum requirements should be noted.)

Education

• Bachelor’s degree in Business, Engineering, Data/Digital, or related field (advanced degree preferred) 

Experience (12–14+ years)

• Experience in pharmaceutical, life sciences, healthcare, or other regulated industries required
• Strong understanding of commercial data domains and processes within pharma/life sciences environments preferred 

• Experience supporting Commercial functions such as Customer Experience, Patient Services, Medical, Digital Health, Market Access, or Content platforms preferred 

• Proven experience in enterprise data strategy, governance, metadata management, interoperability, and AI-ready data ecosystems 

• Experience with cloud and enterprise data platforms (AWS, Databricks, enterprise data platforms) 

• Familiarity with AI/GenAI data requirements including RAG, semantic search, contextualized data, and AI-driven use cases 

• Exposure to ontology, semantic layers, knowledge graph, and enterprise metadata management concepts preferred 

• Proven ability to work across matrixed global organizations and influence senior business and technology stakeholders

Core Skills & Leadership Capabilities

• Strategic and enterprise-level thinking 

• Strong analytical and data-driven decision-making capabilities 

• Ability to translate complex technical concepts into business-relevant outcomes 

• Strong stakeholder management and influencing capabilities 

• Excellent communication and storytelling skills 

• Strong cross-functional orchestration and governance leadership 

• Self-driven with the ability to structure, prioritize, and deliver in complex enterprise environments 

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