Sr. Data & AI Analyst

Job Summary

The Data AI Architect designs and builds the AI-ready context layer that translates Commercial business knowledge, domain rules, and data into structured, governed context that both people and AI agents can reliably act on. This role owns the design of ontological models, taxonomy frameworks, business glossaries, metadata management practices, and business/AI context layers, and the semantic governance processes that keep them current - enabling consistent interpretation of Commercial data and business logic across Sales, Marketing, Market Access, Medical Affairs, Regulatory Affairs, Finance, Analytics, AI/ML Ops, and data product teams. The role owns the Commercial semantic and context layer, partnering with Enterprise IT to build and extend it within the platforms and standards Enterprise IT defines. As Commercial increasingly relies on AI agents and copilots to reason over Commercial data, this role is directly responsible for ensuring those agents are grounded in accurate, governed, and up-to-date business context.

Key Responsibilities

• Design, build, and govern the Commercial semantic and AI context layer in partnership with the Commercial Data Architects and Enterprise IT, translating Commercial business knowledge, processes, and data into structured context that underpins consistent interpretation by both people and AI agents across Sales, Marketing, Market Access, Medical Affairs, Regulatory Affairs, Finance, Analytics, and data product teams.
• Develop and maintain ontological models that represent Commercial domain knowledge structures, including products, stakeholders, channels, outcomes, metrics, business events, and their interrelationships, enabling consistent reasoning across systems, analytical platforms, data products, and AI-enabled solutions.
• Design and maintain taxonomy frameworks and classification structures for Commercial content, data assets, business processes, domains, entities, and use cases, enabling reliable search, segmentation, reporting, reuse, and alignment with pharma commercial operations.
• Build, maintain, and govern the Commercial business glossary, ensuring critical business terms, commercial KPIs, data definitions, business rules, calculations, and regulatory concepts are clearly defined, consistently used, and aligned with approved business meaning.
• Establish and apply metadata management practices for Commercial data assets, including ownership, stewardship, source system, refresh frequency, sensitivity classification, lineage, quality expectations, access considerations, and usage restrictions.
• Translate business rules, metric definitions, KPIs, and domain knowledge into the structured context - knowledge graphs, retrieval-grounding documents, and tool/function schemas - that AI agents and copilots consume, ensuring agent outputs stay grounded in approved Commercial business meaning.
• Partner with the AI/ML Ops team to define how agents access, retrieve, and reason over Commercial context, and to design evaluation approaches that catch ungrounded or inaccurate agent outputs before they reach business users.
• Own context quality, freshness, and drift management as agentic AI use scales across Commercial data products, proactively identifying where business change has outpaced the context layer.
• Architect and maintain the business/AI context layer that translates technical Commercial data structures into business-ready, context-enriched representations consumed by self-service analytics, consistent reporting, data products, and AI agents - designing for reuse across the Commercial organization rather than one-off, single-purpose context.
• Maintain semantic mappings between business concepts, glossary terms, data models, source systems, data products, reports, and AI context assets to improve traceability, discoverability, reuse, and impact analysis across the Commercial data ecosystem.
• Operate semantic governance processes for ontologies, taxonomies, glossaries, metadata standards, context assets, and semantic mappings, ensuring changes are reviewed, documented, versioned, auditable, and aligned with enterprise data governance expectations.
• Partner with data architects, data engineers, product owners, analysts, governance teams, privacy/compliance partners, and business stakeholders to validate definitions, resolve semantic ambiguity, and ensure context assets reflect approved Commercial business meaning.
• Ensure compliance-aware documentation and governance practices by confirming semantic and AI context assets reflect applicable privacy, data use, auditability, access control, and pharma regulatory considerations in a regulated life sciences environment.

How We Work

Where you work matters - because collaboration, innovation and patient impact happen in many settings. Our roles are structured across four work models: site-essential, site-by-design, field-based and remote-by-design. The model assigned to this role is based on its core responsibilities.