Role: Ontologist — Insurance Domain
Job Type: Full-time/Permanent
Location: London - Hybrid
Description:
We have an urgent requirement for Insurance specific ontology specialists who understand insurance domain very well. Someone who has worked extensively with Json structures and can understand how data models and Json structure works may fit in the role..
About the Role
We are building a canonical insurance ontology that spans policy, coverage, underwriting, claims, and product taxonomy — intended to serve as the semantic backbone across our data platforms, AI applications, and regulatory reporting. You will own the vocabulary: designing, extending, and governing OWL/RDF ontologies that give every downstream system a shared, machine-readable.
This is a hands-on engineering role, not a purely theoretical one. You will work in JSON-LD and Turtle daily, collaborate with product SMEs to capture domain semantics correctly, and apply formal upper ontologies (gist, PROV-O) to anchor client terms to open standards.
Required (Must Have)
- 4+ years of hands-on ontology engineering using OWL 2, RDF, RDFS, and SPARQL.
- Proficiency in at least one serialization format: JSON-LD, Turtle, or RDF/XML — JSON-LD pre
- Experience mapping source data schemas (any format) to ontology terms and articulating the design decisions behind each choice.
- Familiarity with at least one upper or mid-level ontology: gist, FIBO, BFO, PROV-O, Dublin
- Ability to communicate ontology decisions in plain business language — you will present to underwriters, not only to engineers.
- Strong written skills: every term you mint needs a clear, non-circular rdfs:comment
Preferred
- Insurance domain knowledge — commercial and consumer insurance products
- Experience with FIBO (Financial Industry Business Ontology) or ACORD data standards and the underlying data models.
- Knowledge of SHACL for ontology constraint validation.
- Familiarity with provenance capture patterns (PROV-O Activities, Entities, Agents).
- Experience building or governing ontologies used in AI / knowledge graph / RAG contexts.
- Python scripting for ontology tooling