Insurance Ontology Specialist

The Opportunity

We are looking for experienced Ontology Specialists with deep insurance domain expertise, particularly in underwriting, claims, policy administration, distribution, risk, pricing, exposure management, reinsurance and insurance operations.

Our clients are establishing enterprise ontologies as a foundation for:

1. Data ontology and semantic layer for GenBI - creating a business-friendly, governed semantic layer that allows executives, underwriters, claims leaders, actuaries, risk teams and operations leaders to ask trusted questions of enterprise data.
2. Operational ontology for transactional workflows - modelling insurance concepts, events, decisions, hand-offs, exceptions and controls so intelligent workflows, AI agents and automated decisioning can be embedded into core operations.
3. AI-ready enterprise knowledge architecture - building the structured business meaning required for GenAI, RAG, knowledge graphs, agentic AI, explainable analytics and intelligent process orchestration.

This is not a pure data modelling role. It is a business architecture, domain modelling, semantic design and consulting role for people who understand how insurance actually works.

Role Purpose

The Ontology Specialist will help insurance clients define, design and operationalize enterprise ontologies that connect business language, data assets, process flows, decision logic and AI use cases.

Insurance domain expertise

Underwriting, claims, policy lifecycle, broker/intermediary models, customer, risk, exposure, coverage, pricing, reserving, fraud, litigation, recoveries and reinsurance.

Semantic and ontology design

Business glossaries, taxonomies, ontologies, knowledge graphs, semantic layers, entity models, relationship models and data meaning.

AI and GenBI enablement

Helping clients move from fragmented reporting and data silos to governed, explainable and AI-consumable business knowledge.

Operational transformation

Embedding ontology into workflows, decisions, controls, exception management and human-AI operating models.

Key Responsibilities

1. Insurance ontology design

• Design and develop insurance-specific ontologies covering customer, party, organisation and intermediary structures.
• Model policy, product, coverage, endorsement, contract, exposure, peril, location, asset and risk object concepts.
• Model underwriting submission, risk assessment, quote, bind, renewal, appetite, referral and portfolio steering journeys.
• Model claims FNOL, triage, coverage validation, liability, reserving, settlement, recovery, litigation and leakage concepts.
• Translate complex insurance language into semantic models used by business, data, analytics, AI and workflow teams.

2. Semantic layer for GenBI

• Define common business metrics, dimensions, hierarchies and executive KPI definitions.
• Align executive KPIs to underlying data concepts and resolve inconsistencies across functions, systems and geographies.
• Model relationships between customer, policy, claim, coverage, risk, broker, exposure and transaction entities.
• Create explainable definitions for loss ratio, expense ratio, claims leakage, underwriting profitability, conversion, retention, STP and operational productivity.
• Enable GenBI use cases where business users can query enterprise data using natural language with trusted semantics.

3. Operational ontology for transactional workflows

• Model business events, process states, decision points, controls, approvals, exception paths and case types.
• Define SLA triggers, human and AI hand-offs, business rules, data dependencies, audit requirements and explainability needs.
• Help clients move from document-heavy process maps to intelligent operating models where ontology supports automation, agentic workflows and decision orchestration.

4. Underwriting and claims domain modelling

• Bring deep SME insight into underwriting or claims journeys and the meaning behind core data, decisions and workflow states.
• For underwriting: model submission intake, appetite, triage, enrichment, pricing, referrals, quote-bind, renewal and portfolio steering.
• For claims: model FNOL, segmentation, coverage validation, liability, reserving, fraud, litigation, vendor management, settlement and recovery.
• Support workbench, platform and workflow design by creating business-level models rather than simply system-level data structures.

5. Knowledge graph and AI enablement

• Define insurance entities and relationships for knowledge graphs and graph-based reasoning.
• Support RAG and GenAI solutions with governed domain knowledge and contextual enrichment.
• Help define AI agent memory, context, guardrails, decision boundaries, explainability and traceability.
• Create reusable ontology assets for underwriting, claims, operations and enterprise AI enablement.

6. Consulting and client leadership

• Facilitate workshops with underwriters, claims leaders, data owners, architects, operations teams and AI leaders.
• Translate business pain points into ontology, semantic architecture and operating model requirements.
• Create senior stakeholder-ready outputs including ontology blueprints, semantic models, roadmaps and executive recommendations.
• Support pre-sales, proposition development and thought leadership for insurance clients.

Required Experience

We are looking for candidates with a combination of insurance domain expertise, semantic modelling experience and consulting capability.

Essential

• Strong experience in insurance, reinsurance or specialty insurance.
• Deep understanding of at least one major domain: underwriting, claims, policy, distribution, risk, pricing or reinsurance.
• Experience defining business taxonomies, data dictionaries, conceptual models, information models, ontologies or semantic layers.
• Ability to translate complex insurance concepts into structured, reusable and business-consumable models.
• Strong understanding of data relationships across policy, customer, claim, risk, broker, exposure and transaction entities.
• Experience working with business SMEs, data teams, architects, product owners and technology delivery teams.
• Ability to run client workshops and convert ambiguity into structured, executive-ready outputs.
• Consulting mindset: structured thinking, stakeholder management, problem solving and senior communication.

Preferred

• Experience with knowledge graphs, graph databases or semantic technologies.
• Familiarity with RDF, OWL, SHACL, SPARQL or related ontology standards.
• Experience with Neo4j, Stardog, GraphDB, PoolParty, TopBraid, Palantir Foundry/AIP, Databricks, Snowflake, Microsoft Fabric or similar platforms.
• Experience building semantic models for BI, GenBI or natural language querying.
• Experience supporting GenAI, RAG, AI agent or decision intelligence initiatives.
• Experience with ACORD, insurance canonical models or industry data standards.
• Prior consulting experience with insurers or reinsurers.
• Experience with Guidewire, Duck Creek, Majesco, Sapiens, EIS, Salesforce Financial Services Cloud or underwriting/claims workbench platforms.
• Understanding of regulatory, audit, model risk or data governance requirements in insurance.

Candidate Profiles We Want to Meet

• Insurance data architects or information architects.
• Underwriting data model specialists or claims data model specialists.
• Knowledge graph specialists with insurance experience.
• Semantic layer architects or ontology engineers in financial services.
• Business architects in insurance transformation programmes.
• Senior business analysts with strong insurance domain modelling experience.
• Product owners for underwriting, claims or insurance data platforms.
• Ex-insurance practitioners who have moved into data, AI or architecture roles.