Senior Data Scientist - Ontology

The Ontology Engineer is a foundational technical hire on the AI, ML and Data Science team specializing in Knowledge Representation. This role is responsible for designing and maintaining the formal ontological architecture that makes cross-organizational data alignment. This is not a taxonomy or metadata management role. It requires genuine formal depth in description logics, upper ontology theory, and the ability to reason about what an ontology commits to and what it leaves open. Our platform sits between hospitals, distributors, GPOs, manufacturers, and regulators, enabling transactional execution, clinical data alignment, and analytics optimization across organizational boundaries. Each party maintains its own implicit ontology encoded in its schemas, workflows, and data. The Ontology Engineer will define the formal structures and processes that make alignment across them possible. These structures should be auditable, compositionally sound, and maintainable over a multi-year lifecycle as all parties' systems evolve. This Engineer will work directly with teammates that are familiar with ontology formalisms and with domain experts who understand the operational realities of HCSC data. They will be expected to make and defend design decisions at the level of formal correctness, not just practical convenience, and to direct and evaluate LLM-assisted ontology discovery and enrichment pipelines with the rigor that formal alignment demands. Essential Duties: - Design and maintain the ontology, covering the canonical structural layer (organizations, items, contracts, transaction), source data ontologies (supporting the canonical) and the process layer (data curation, ontology matching, workflows). - Establish the rules for when two records from different systems refer to the same thing, and when they don't — recognizing the answer can differ by use case. - Establish mappings from trading partner source data to the canonical ontology, with documented provenance and validity conditions for each mapping. - Author OWL 2 axioms for ontology components; validate logical consistency (e.g. reasoner); maintain ontology lifecycle (e.g. with ROBOT, SHACL). - Align with governance team and practice. - Grounded ontology discovery from data (and its uses) rather than schema declarations and metadata alone. - Build, direct and evaluate LLM-assisted ontology extraction pipelines, define and enforce the human-in-the-loop validation standards for AI-generated ontological candidates. - Collaborate with data quality engineers to establish formal feedback . - Translate formal ontology design decisions into specification/implementation for graph and relational stores. - Specify and implement SPARQL queries and graph schema requirements with sufficient precision to prevent implementation-level semantic loss. - Collaborate with internal and external stakeholders including domain experts, data engineers, product managers, and integration partners to ensure ontological architecture supports transactional, clinical, and analytical requirements. - Proactively monitor developments in formal ontology, knowledge representation, and LLM-assisted knowledge engineering to drive adoption of improved methods. Competencies: - Fluency in OWL 2 and description logics: able to read and write OWL axioms, understand what a reasoner computes and why, and diagnose inference failures without relying solely on tooling. - Working knowledge of at least one upper ontology (e.g. BFO) and the ability to apply upper ontology commitments to a domain ontology correctly, including the continuant/occurrent distinction. - Proficiency in knowledge graph technologies including RDF, OWL, and SPARQL; familiarity with property graph approaches (LPG, Cypher) and awareness of the semantic differences between RDF-based and property graph representations. - Understanding of data integration: schema matching and mapping semantics, entity resolution, and the formal propertie