Director of Applied Engineering

The Hartford is seeking a Director of Applied Engineering within Applied Analytics to lead a high-impact team of Analytics Engineers driving the next generation of AI-powered intelligence across the enterprise. This role involves building and growing a team that designs and delivers sophisticated AI solutions for business users to explore complex insurance data and derive actionable insights.

Responsibilities:

• Build, lead, and develop a team of Analytics Engineers, fostering a culture of innovation, technical excellence, and continuous learning focused on agentic analytics and conversational AI
• Set clear goals, provide ongoing coaching and feedback, and create development pathways that grow the team's capabilities in AI/ML and domain expertise
• Recruit and retain top-tier analytics engineering talent with experience in generative AI, LLMs, RAG pipelines, and BI design
• Champion a collaborative, psychologically safe team environment that encourages experimentation and responsible AI development
• Define and own the multi-year roadmap for the Applied Analytics function, aligning agentic analytics, conversational AI, and BI initiatives with enterprise data strategy and business priorities
• Lead disciplined innovation by balancing delivery excellence with forward-looking investment in emerging AI/analytics technologies and methodologies
• Establish the team's technical standards, architectural patterns, and governance frameworks for AI solution development and MLOps practices
• Drive the adoption of agentic AI workflows — including multi-agent orchestration, tool-use patterns, and autonomous analytics — across the Applied Analytics team
• Own the strategic direction for conversational AI capabilities that allow business users to explore insurance data and derive insights through natural language interfaces
• Guide the team in designing and delivering RAG pipelines, intelligent chat/assistant systems, classification, forecasting, and recommendation engines - leveraging a fit-for-purpose toolkit from traditional ML to sophisticated agentic workflows
• Set the architectural vision for agent design, including prompt engineering standards, safe tool-use policies, function/structured calling patterns, and guardrails for reliable and ethical agent behavior within the insurance context
• Champion responsible AI practices including fairness, bias mitigation, transparency, observability, and compliance-by-design across all conversational and agentic solutions
• Lead the strategy and governance for AI-driven BI across insurance lines of business, ensuring consistent, accurate, and business-friendly definitions of facts, dimensions, and metrics
• Partner with data engineering, platform, and architecture teams to ensure BI solutions are scalable, maintainable, and directly consumable by AI agents and BI tools
• Drive the team's use of dimensional modeling and BI best practices to create a unified view of complex insurance data that accelerates both analytical and AI use cases
• Lead the delivery of GenAI capabilities supporting regulatory filing automation, including DOI objection response generation and ingestion of legacy filings into searchable knowledge bases
• Ensure the team embeds domain taxonomies, regulatory constraints, access controls, and security directly into solution design
• Partner closely with Legal and Compliance to meet evolving standards
• Oversee the engineering and maintenance of domain-specific knowledge bases (e.g., regulatory intelligence, competitive insights, customer sentiment) to power generative applications across underwriting, pricing, and service
• Lead the team through the full AI solution lifecycle: problem framing, data preparation, model development, evaluation, CI/CD, orchestration, observability, safety, and rollback
• Establish and enforce GitHub best practices for version control, documentation, and code collaboration across the analytics engineering lifecycle
• Drive standardization of experiment tracking, model registries, evaluation gates, and CI/CD patterns across cloud platforms
• Oversee the team's evaluation and monitoring practices — ensuring comprehensive metrics coverage across RAG/chat, classification, forecasting, and operational KPIs — and champion A/B testing and drift detection as standard practice

Requirements:

• 8+ years of relevant experience in analytics engineering, data science, or AI/ML, with at least 3 years in a people management role leading technical teams
• Demonstrated experience building and developing high-performing teams in an Agile environment, including hiring, coaching, performance management, and career development
• Proven track record delivering production AI/ML solutions, including conversational AI systems, agentic workflows, or RAG pipelines in an enterprise setting
• Strong understanding of BI principles: dimensional modeling, fact tables, metrics definition, and data warehouse/data lake architectures
• Experience designing and executing a multi-year technical roadmap for an analytics or AI function, including prioritization, resource planning, and stakeholder alignment
• Proficiency in Python and SQL; ability to engage credibly with technical teams on data preparation, model development, and evaluation approaches
• Experience with cloud platforms (GCP Vertex AI, AWS SageMaker/Bedrock, or Azure AI Services) and modern data platforms (Snowflake, Redshift, or equivalent)
• Strong familiarity with MLOps practices: CI/CD for ML, experiment tracking, model registries, evaluation frameworks, and observability
• Experience with responsible AI principles: fairness, bias mitigation, transparency, observability, and compliance-by-design
• Bachelor's degree in Computer Science, Data Science, Engineering, Applied Mathematics, or a related analytical field
• Candidate must be authorized to work in the US without company sponsorship