Senior Ontologist role on Samba TV's Knowledge Graph & Identity team. Responsible for design, development, and governance of semantic data models and ontological frameworks foundational to Samba's knowledge graph. Hands-on technical work includes ontology design, SPARQL coding, knowledge graph pipeline development, and collaboration with data engineering and data science teams. Utilize ML and AI techniques including embedding-based and LLM-augmented methods for ontology mapping and entity resolution. Requires 5-8 years experience in ontology engineering or knowledge graph development, expertise in RDF, RDFS, OWL, SPARQL, SHACL, strong Python skills, and experience with entity resolution at scale. Bachelor's degree required; advanced degree preferred. Located in New York City.
What you'll do
Own design, development, and versioning of Samba TV's core ontologies in RDF/RDFS/OWL including entity classes, properties, hierarchies, and constraints
Author and maintain SHACL shapes for graph validation, consistency checking, and data quality enforcement
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Requirements
5–8 years of hands-on experience in ontology engineering, semantic data modeling, or knowledge graph development with production ontologies at scale
Deep expertise in W3C semantic web standards: RDF, RDFS, OWL, SPARQL 1.1, and SHACL with hands-on experience building and validating graph schemas in a production triplestore (Amazon Neptune, Stardog, GraphDB, Jena, or equivalent)
Strong Python skills with production-quality, well-tested code; comfortable building data pipelines and graph processing workflows
First-principles understanding of description logics, ontology design patterns, and trade-offs between OWL expressivity and triplestore scalability
Hands-on experience with entity resolution, record linkage, or deduplication at scale
Bachelor's degree in Computer Science, Information Science, Computational Linguistics, Mathematics, or related field; Master's or PhD strongly preferred
Strong communication skills to defend ontological modeling decisions and explain trade-offs to non-specialists
