Underwriting Ontologist – Claims, Underwriting, Insurance

A large global organisation is seeking an experienced Underwriting & Finance Ontologist to design, develop and support enterprise-grade ontology and semantic data solutions across Insurance and Finance.

The ideal candidate will possess deep expertise in ontology development within the financial services or insurance sector, with particular experience modelling financial data domains such as FP&A, Controllership, General Ledger (GL), Sub-ledgers, and the financial impact of insurance policies and claims.

This will be a permanent position, the ideal candidate will be based in London, UK.

This position can be worked remotely, hybrid or on an on-site basis.

• Design and develop enterprise insurance ontologies covering:
• Build and maintain semantic models, taxonomies, and knowledge graphs.
• Define entity relationships, business vocabularies, and metadata standards.
• Develop reusable ontology frameworks supporting enterprise AI and analytics initiatives.
• Integrate ontology models with enterprise systems, APIs, and data platforms.
• Support linked data and semantic interoperability initiatives.
• Collaborate with data engineering and architecture teams on enterprise data models.
• Support master data management and governance initiatives.

• Translate underwriting and claims business requirements into semantic models.
• Support digital transformation, automation, and AI-driven insurance initiatives.
• Partner with underwriting, claims, and operational SMEs to model complex insurance concepts.
• Perform impact analysis on data, process, and reporting changes.

• Define ontology governance, naming conventions, and modelling standards.
• Ensure consistency and quality across enterprise semantic assets.
• Maintain ontology documentation and data lineage standards.
• Align solutions with industry standards and regulatory requirements where applicable.
• Support ontology lifecycle management and ongoing enhancements.
• Troubleshoot semantic data and integration issues.
• Conduct root-cause analysis and implement long-term fixes.
• Support releases, testing, and production deployments.

• OWL, RDF, SPARQL, SKOS
• Knowledge Graph technologies
• Semantic modelling and taxonomy development
• Graph databases and linked data concepts
• API integration & enterprise data flows