Senior Data Scientist – Ontology

🔥 0 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

📊 Data Scientist

👻 Ghost score 10%

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GHX

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2010

💼 Consulting

📦 Logistics

🏥 Healthcare

Consulting • Logistics • Healthcare

GHX is a leading provider of data management solutions, supply chain consulting, and vendor credentialing services primarily for the healthcare industry. They offer innovative products such as order automation, inventory management, and e-payment solutions that enhance financial agility, compliance, and operational efficiency. GHX serves a diverse clientele including healthcare providers, suppliers, and government agencies, emphasizing evidence-based insights and cloud ERP transformation to optimize resource allocation, reduce costs, and improve patient care. They are deeply integrated into the healthcare supply chain, collaborating with partners to create secure, informed, and cost-effective environments in healthcare facilities.

📋 Description

• Design and maintain the ontology across canonical structural, source data, and process layers • Establish rules for determining when records from different systems refer to the same entity, depending on use case • Create mappings from trading partner source data to the canonical ontology with documented provenance and validity conditions • Author OWL 2 axioms and validate logical consistency using reasoners • Maintain ontology lifecycle using tools such as ROBOT and SHACL • Align with the governance team and practice • Discover ontologies from data rather than relying only on schema declarations and metadata • Build, direct, and evaluate LLM-assisted ontology extraction pipelines • Define and enforce human-in-the-loop validation standards for AI-generated ontological candidates • Collaborate with data quality engineers to establish formal feedback • Translate formal ontology decisions into specifications and implementations for graph and relational stores • Specify and implement SPARQL queries and graph schema requirements • Collaborate with domain experts, data engineers, product managers, integration partners, and other stakeholders • Monitor developments in formal ontology, knowledge representation, and LLM-assisted knowledge engineering

🎯 Requirements

• Greater than 4 years of experience in knowledge engineering, ontology development, or a closely related formal methods discipline • Fluency in OWL 2 and description logics • Working knowledge of at least one upper ontology, such as BFO • Proficiency in RDF, OWL, and SPARQL; familiarity with LPG and Cypher • Understanding of schema matching, mapping semantics, entity resolution, and multi-source alignment • Ability to interpret functional dependencies and inclusion dependencies as ontological signals • Familiarity with LLM-assisted ontology extraction and enrichment pipelines • Demonstrated experience building and maintaining domain ontologies in Protege or equivalent, with reasoner-validated consistency • Experience with ROBOT or ODK, or similar ontology lifecycle management tooling • Expertise in SPARQL and/or Cypher • Experience translating data profiling output into formal ontological claims • Experience with empirical ontology discovery and top-down ontology design • Experience directing or evaluating LLM-assisted knowledge extraction pipelines with formal validation requirements • Proficiency in Python or similar • Experience working in multidisciplinary teams • Bachelor's or advanced degree in a relevant discipline is preferred, not required • Familiarity with category theory, CQL/AQL, or categorical database theory is preferred or a plus • Experience with LinkML is preferred • Healthcare supply chain domain knowledge is preferred • Experience with BFO 2.0 and OBO Foundry principles is preferred • Familiarity with provenance models is preferred • Experience with production-scale graph database platforms is preferred

🏖️ Benefits

• Equal employment opportunities and consideration without regard to protected status • Working environment intended to enable employees to be productive and work to the best of their ability • Comprehensive benefits are referenced in the EEO policy, but no specific benefits are listed