Senior Data Scientist - Ontology

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 properties of multi-source alignment.
• Ability to interpret data profiling results (functional dependencies, inclusion dependencies) as ontological signals rather than purely as data quality metrics.
• Familiarity with LLM-assisted ontology extraction and enrichment pipelines, including the ability to evaluate LLM-generated ontological candidates against formal.
• Excellent communication skills for translating formal design to business stakeholders without losing precision and to engineers without losing formal correctness.
• Comfort working with partial/incomplete formal models, maintaining clear documentation of what remains unspecified and why.
• Requires minimal to no supervision on formal ontology design work.

Required Qualifications and Skills:

• Greater than 4 years of experience in knowledge engineering, ontology development, or a closely related formal methods discipline.
• Demonstrated experience building and maintaining domain ontologies in Protege or equivalent, with reasoner-validated consistency; not solely taxonomy or metadata management work.
• Experience with ROBOT or ODK for ontology lifecycle management (or similar): automated quality checks, versioning, release pipelines.
• Expertise in SPARQL and/or Cypher for querying ontology-aligned data stores; ability to write and evaluate queries that correctly reflect ontological intent.
• Demonstrated ability to interpret data profiling output and translate it into formal ontological claims; experience with empirical ontology discovery from data as well as top-down ontology design.
• Experience directing or evaluating LLM-assisted knowledge extraction pipelines with formal validation requirements.
• Proficiency in Python (or similar) for ontology tooling, pipeline scripting, and data analysis in support of knowledge engineering workflows.
• Experience working in multi-disciplinary teams where formal and domain knowledge must be integrated under operational constraints.

Preferred Qualifications and Skills:

• Bachelor's or advanced degree in Computer Science, Mathematics, Philosophy (logic/formal methods), Information Science, or a related hard science discipline.
• Familiarity with category theory as applied to data integration -- functors, natural transformations, limits and colimits as schema merge operations -- at literacy level or above; knowledge of CQL/AQL or categorical database theory is a plus.
• Experience with LinkML.
• Healthcare supply chain domain knowledge and ontological structures.
• Experience with BFO 2.0 and the OBO Foundry principles and standards.
• Familiarity with provenance models (why-provenance, how-provenance, where-provenance) and their implementation in ontology-aligned data systems.
• Experience with graph database platforms at production scale (Stardog, Amazon Neptune, or equivalent) and the operational considerations of ontology-driven graph deployments.
• Passion for staying at the cutting edge of knowledge representation, semantic alignment, and AI-assisted ontology engineering.
• Sense of humor.