Sr Applied AI Scientist, Knowledge Graph

high-impact proof points such as fraud and designing for enterprise reuse across domains.

Impact & Scope
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

What You Will Do
Applied Research & Prototyping:

• 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

• Graph Foundations & Engineering Collaboration:

• 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

• Evaluation & Responsible AI:

• Design evaluation frameworks for reasoning quality, robustness, and traceability

• Contribute reusable patterns, documentation, and integration guidance

• Collaboration

You will work closely with data scientists, engineers, platform teams, architects, and business stakeholders to turn advanced methods into scalable, adoptable AI products with measurable outcomes.

Required Qualifications

• 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

Preferred Qualifications:

• 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

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:
$110,720 - $166,080
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age