Sr Applied AI Scientist, Knowledge Graph

The Hartford•Columbus, OH

20h

About The Position

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. Step into the future with The Hartford as a Sr Applied AI Scientist, where Generative AI and advanced reasoning are core strategic capabilities—not side projects. Join the AI Accelerator, a team built to convert advanced AI into enterprise-ready reusable solutions across underwriting, claims, and pricing, while embedding compliance, trust, and auditability into every product we deliver. In this role, you will help shape a foundational capability for relationship-aware AI by building and scaling knowledge graph and graph-reasoning approaches—starting with high-impact proof points such as fraud and designing for enterprise reuse across domains. You will play a key role in advancing the enterprise Knowledge Graph strategy, focused on: Enterprise reuse through governed, reusable knowledge assets Vendor independence via enterprise-owned graph capabilities Explainable reasoning using neuro-symbolic and graph-based approaches Target-state architecture that decouples ontology, entity resolution, graph storage, and reasoning to maximize reuse across fraud, risk, underwriting, and analytics.

Requirements

Master’s or PhD in Computer Science, AI/ML, Data Science, Engineering, or a related field
3+ years (PhD) or 5+ years (MS) of industry experience in applied AI, ML, or data science
Demonstrated experience with knowledge graphs or graph-based techniques such as ontology modeling, entity resolution, graph analytics, embeddings, GNNs, or reasoning engines
Strong proficiency in Python and modern ML/data tooling
Ability to design experiments and clearly communicate tradeoffs to technical and non-technical audiences
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.

Nice To Haves

Experience with graph technologies and standards (RDF/OWL, Cypher, SPARQL, Gremlin)
Familiarity with Graph RAG, agentic workflows, or tool-augmented LLMs
Experience operationalizing AI/ML systems in production environments
Background in explainable AI, symbolic reasoning, or enterprise AI governance

Responsibilities

Conduct applied research in knowledge graphs, graph reasoning, and neuro-symbolic AI
Prototype graph-enabled solutions for real business problems such as fraud detection and relationship discovery
Evaluate architectures and reasoning strategies to inform platform and roadmap decisions
Define and implement ontology and semantic modeling patterns
Design entity resolution and linkage approaches
Partner with engineering, platform, and MLOps teams to operationalize graph capabilities with enterprise-grade standards
Design evaluation frameworks for reasoning quality, robustness, and traceability
Contribute reusable patterns, documentation, and integration guidance
Work closely with data scientists, engineers, platform teams, architects, and business stakeholders to turn advanced methods into scalable, adoptable AI products with measurable outcomes.