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Tier-1 employer, mid-level experience requirement, metro location increases applicant density despite niche specialization.
Medium — core semantic and governance skills transferable, but pharma regulatory knowledge increases specificity.
High due to explicit 5+ years, required semantic modeling/governance skills, and regulated pharma domain experience.
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Design, implement, maintain, and govern the enterprise semantic layer supporting Commercial data strategy across multiple functions including Sales, Marketing, Market Access, Medical Affairs, Regulatory Affairs, Finance, and Analytics.
Develop and operationalize ontologies, taxonomies, business glossaries, metadata management, business context layers, and semantic governance processes to enable consistent commercial data interpretation and AI-ready data assets.
Collaborate with data architects, engineers, product owners, business stakeholders, and compliance teams to ensure semantic assets reflect approved business meaning and regulatory/privacy requirements in a life sciences environment.
Bachelor's degree in Computer Science, Information Science, Life Sciences, Data Science, Information Systems, Engineering, or related field; Master's preferred in relevant disciplines.
5+ years' experience in data management, information architecture, semantic modeling, data governance, or knowledge engineering, preferably in pharmaceutical, biotech, life sciences commercial or regulated environments.
Hands-on experience with ontology development, taxonomy design, business glossary management, metadata management, semantic mappings, and related governance processes.
Familiarity with semantic technologies, semantic tools (e.g., RDF, OWL, SPARQL), data catalog/governance tools (e.g., Collibra, Alation), cloud data platforms (e.g., Snowflake, Databricks), and Commercial pharma data ecosystems is preferred.
Experienced in designing and governing semantic layers for complex commercial data ecosystems in regulated life sciences or pharmaceutical domains.
Strong competency in semantic modeling and knowledge representation with experience integrating technical data models into business context and AI-ready assets.
Collaborates effectively across data architecture, engineering, analytics, governance, and compliance teams to ensure consistent, compliant, and discoverable data definitions and assets.