[Hiring] Principal Ontologist and Graph Modeler @The Hartford

Role Description

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.

• Ensure that Business Insurance concepts, entities, relationships, metrics, rules, and behaviors are explicitly modeled and consistently applied across data products and AI systems.
• Enable reuse, trust, reasoning, semantic interoperability, and scalable AI adoption across various domains.
• Translate complex insurance domain knowledge into production-ready semantic assets consumable by both humans and machines.

• Lead the design and execution of enterprise-scale semantic layers for the Business Insurance data portfolio.
• Define and operationalize ontologies, context graphs, and knowledge graphs across Business Insurance domains.
• Model core insurance concepts and relationships spanning various areas.
• Enable semantic-first AI and agentic analytics.
• Define canonical semantic vocabularies that standardize meaning across data sources.
• Drive production-scale execution of semantic and graph platforms.
• Define and promote standards and reusable patterns for ontology modeling and graph modeling.
• Partner with data product owners, domain leaders, architects, and engineers.
• Work closely with AI Data Architects and AI Data Engineers.
• Align ontologies and graph models with enterprise data governance standards.
• Enable explainability by ensuring AI outputs can be traced to governed definitions.
• Serve as the principal authority for semantic engineering practices.
• Advance agentic data engineering through reference patterns and production deployments.
• Contribute to communities of practice and internal enablement materials.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Information Management, or a related field, or equivalent work experience.
• 8–12+ years of hands-on experience in semantic layer architecture, ontology modeling, graph modeling, and knowledge graph design.
• Deep expertise with RDF, OWL (OWL 2), RDFS, SKOS, SPARQL, and W3C semantic web standards.
• Proven experience designing and operating enterprise knowledge graphs.
• 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 and agentic frameworks.
• Strong understanding of AI consumption patterns.
• Experience integrating semantic layers with data platforms and APIs.
• Ability to translate complex insurance domain knowledge into formal semantic structures.
• Strong understanding of context-aware data engineering and semantic interoperability.
• Proven ability to move from strategy to scaled enterprise capability.
• Strong executive influence and thought leadership in semantic engineering.
• Hands-on experience with AWS, GCP, and Snowflake.
• Business Insurance domain knowledge across one or more areas.
• Excellent communication, presentation, collaboration, and technical leadership skills.
• Candidate must be authorized to work in the US without company sponsorship.

Requirements

• The company will not support the STEM OPT I-983 Training Plan endorsement for this position.

Benefits

• The listed annualized base pay range is primarily based on analysis of similar positions in the external market.
• Actual base pay could vary based on performance, proficiency, and demonstration of competencies.
• Base pay is just one component of the total compensation package.
• 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: $145,440 - $218,160.

Equal Opportunity

• Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age.