We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.   

The Principal Ontologist and Graph Modeler is responsible for designing, implementing, and operationalizing enterprise semantic models, ontologies, and knowledge representations that provide meaning, context, and explainability for AI-driven analytics, agentic systems, and decision automation across the Business Insurance data portfolio.

This role ensures that Business Insurance concepts, entities, relationships, metrics, rules, and behaviors are explicitly modeled and consistently applied across data products and AI systems. The role enables reuse, trust, reasoning, semantic interoperability, and scalable AI adoption across underwriting, policy, claims, distribution, customer, product, and portfolio domains.

Operating at the intersection of business semantics, data architecture, graph engineering, and AI enablement, this role translates complex insurance domain knowledge into production-ready semantic assets that are consumable by both humans and machines.

Job Responsibilities:

• Lead the design and execution of enterprise-scale semantic layers for the Business Insurance data portfolio to standardize business meaning and enable trusted analytics, AI, and agentic use cases.
• Define and operationalize ontologies, context graphs, and knowledge graphs across Business Insurance domains to power reasoning, explainability, and decision intelligence.
• Model core insurance concepts and relationships spanning customers, accounts, submissions, quotes, policies, coverages, products, risks, claims, producers, and portfolio performance.
• Enable semantic-first AI and agentic analytics by ensuring large language models and agents can consume governed business context, metrics, rules, and relationships.
• Define canonical semantic vocabularies that standardize meaning across structured and unstructured Business Insurance data sources.
• Drive production-scale execution of semantic and graph platforms with strong standards for performance, governance, security, and lifecycle management.
• Define and promote standards and reusable patterns for ontology modeling, graph modeling, semantic interoperability, and ontology reuse across delivery teams.
• Partner with Business Insurance data product owners, domain leaders, architects, and engineers to embed semantic models into data products, AI pipelines, analytics, and activation layers.
• Work closely with AI Data Architects and AI Data Engineers to operationalize ontologies through graphs, metadata services, APIs, and production data platforms.
• Align ontologies and graph models with enterprise data governance, metadata, lineage, quality, privacy, and security standards.
• Enable explainability by ensuring AI and analytical outputs can be traced to governed semantic definitions, relationships, rules, and source data.
• Serve as the principal authority for semantic engineering, ontology, and graph-modeling practices supporting the Business Insurance data portfolio.
• Advance agentic data engineering through reference patterns, playbooks, proofs of value, and production deployments.
• Contribute to communities of practice, reference guidance, reusable assets, and internal enablement materials.

 Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Information Management, or a related field, or equivalent work experience with large scale data warehouses data.
• 8–12+ years of hands-on experience in semantic layer architecture, ontology modeling, graph modeling, and knowledge graph design at enterprise scale.
• Deep, hands-on expertise with RDF, OWL (OWL 2), RDFS, SKOS, SPARQL, and W3C semantic web standards.
• Proven experience designing and operating enterprise knowledge graphs and governed semantic assets.
• Hands-on experience with graph or triple-store technologies such as Neo4j, Amazon Neptune, or Spanner Graph.
• Experience integrating knowledge graphs with large language models, retrieval-augmented generation pipelines, vector stores, and agentic frameworks.
• Strong understanding of AI consumption patterns, including embeddings, grounding, reasoning, and explainability.
• Experience integrating semantic layers with data platforms, APIs, metadata systems, analytics platforms, and AI pipelines.
• Ability to translate complex insurance domain knowledge into formal, machine-readable semantic and graph structures.
• Strong understanding of context-aware data engineering, canonical modeling, and semantic interoperability.
• Proven ability to move from strategy to pilot to scaled enterprise capability.
• Strong executive influence and thought leadership in semantic engineering, agentic analytics, and AI-native data engineering.
• Hands-on experience with AWS, GCP, and Snowflake.
• Business Insurance domain knowledge across one or more areas such as underwriting, policy, claims, distribution, product, customer, or portfolio management.
• Excellent communication, presentation, collaboration, and technical leadership skills.

Candidate must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:

Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

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