Principal Ontologist and Graph Modeler

The Hartford•Hartford, CT

12d

About The Position

The Principal Ontologist and Graph Modeler designs, implements, and operationalizes enterprise semantic models, ontologies, and knowledge representations for AI-driven analytics, agentic systems, and decision automation across the Business Insurance data portfolio. The role ensures that Business Insurance concepts, entities, relationships, metrics, rules, and behaviors are explicitly modeled and consistently applied across data products and AI systems, enabling reuse, trust, reasoning, semantic interoperability, and scalable AI adoption across underwriting, policy, claims, distribution, customer, product, and portfolio domains. Working across business semantics, data architecture, graph engineering, and AI enablement, the position translates complex insurance domain knowledge into production-ready semantic assets that people and machines can use. The role serves as the principal authority for semantic engineering, ontology, and graph-modeling practices supporting the Business Insurance data portfolio. It partners with domain leaders, data product owners, architects, engineers, and AI teams to integrate governed semantic assets into data products, AI pipelines, analytics, and production platforms, while aligning them with enterprise governance, metadata, lineage, quality, privacy, and security standards.

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.
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 in one or more areas such as underwriting, policy, claims, distribution, product, customer, or portfolio management.
Excellent communication, presentation, collaboration, and technical leadership skills.
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.

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 support reasoning, explainability, and decision intelligence.
Model 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, establishing standards for performance, governance, security, and lifecycle management.
Define and promote reusable standards and 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 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 for 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.