Ontology Specialist

Responsibilities

• Design and maintain the knowledge representation that underpins healthcare administrative data.
• Translate tacit domain knowledge into formal RDF/OWL structures.
• Align with industry standards (FHIR, SNOMED, ICD).
• Inform how downstream AI and analytics systems reason over the data.
• Design and maintain ontologies (RDF, OWL, SKOS) for the healthcare administrative domain.
• Map and align internal models against FHIR, SNOMED, and ICD.
• Query and validate the knowledge graph using SPARQL; iterate on representation patterns.
• Codify tacit business knowledge into formal structures, in partnership with domain experts.
• Explain tradeoffs between modeling approaches and recommend a direction.
• Inform graph database design (Neo4j, TypeDB, or similar) and integration with AI systems.

• Bachelor's degree and 7 years in data/knowledge engineering, with significant time in ontology or knowledge representation.
• Hands‑on experience with RDF, OWL, SKOS, or similar.
• Proficiency with SPARQL for querying and manipulating RDF/OWL data.
• Hands‑on experience with an ontology authoring tool (Protégé or equivalent).
• Working knowledge of ontology design patterns.
• Familiarity with healthcare standards (FHIR, SNOMED, ICD) applied to administrative domains.
• Graph database experience (Neo4j, TypeDB, or similar).
• Proficiency in Python and exposure to No
  
  SQL, SQL, and graph databases.
• Familiarity with AI frameworks.

• Pattern codification:
  Translates tacit business knowledge into formal structures.
• Healthcare context:
  Understands clinical and administrative domain complexity.
• Industry awareness:
  Tracks ontology and knowledge graph evolution; brings relevant insights back.
• Experimentation:
  Tests modeling approaches before committing to architecture; explains tradeoffs from hands‑on experience.
• Exposure to reasoners (e.g., HermiT, Pellet, ELK), SHACL for shape/constraint validation, and terminology services (UMLS, terminology servers) is a plus.