Director of Applied Engineering

The Hartford•Hartford, CT

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. 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 sits at the intersection of Artificial Intelligence, Business Intelligence, and deep insurance domain expertise — owning the vision, strategy, and delivery of conversational AI agents, agentic analytics workflows. As Director, you will build and grow a team of Analytics Engineers who design and deliver sophisticated AI solutions that enable business users to explore complex insurance data, derive actionable insights in plain English, and accelerate data-driven decision-making. You are a leader who understands the technical depth required to build these systems and who can translate that depth into a compelling team roadmap, a high-performing culture, and measurable business outcomes.

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

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.