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Tier-1 employer and metro location increase applicant volume, but very niche semantic/ontology skills reduce competition.
Deep pharmaceutical regulatory and operations domain expertise makes cross-industry transfer challenging.
Explicit 8–12 years plus mandatory RDF/OWL/SPARQL and pharma domain expertise enforces strict screening.
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Lead and deliver connected data initiatives aligned with Operations Data Strategy to modernize pharmaceutical regulatory processes.
Develop and formalize semantic business information models and ontologies capturing pharmaceutical operational domains such as Process Development, Quality, Manufacturing, Engineering, and Supply Chain.
Drive data interoperability by designing a connected data ecosystem using semantic modeling, RDF technologies, Generative AI, and knowledge graphs to empower decision-making and digital transformation.
Master's or Bachelor's degree plus 8-12 years of relevant experience.
Proven expertise in semantic modeling, RDF, OWL, SHACL, ontology development, and knowledge graph creation with tools like TopBraid, Protégé, or Stardog.
Deep understanding of pharmaceutical operations data domains including Process Development, Manufacturing, Engineering, Quality, Supply Chain.
Strong technical skills in Python development, SQL data transformation, SPARQL querying, and FAIR data principles application.
Experienced in negotiating and aligning cross-functional pharmaceutical stakeholders on semantic data models and standards.
Operates effectively in regulated pharmaceutical environments with familiarity in regulatory data modeling and compliance.
Strategic thinker capable of bridging technical semantic modeling with operational business needs to drive data-centric digital transformation.