Director, Data Science

Liberty Mutual•Boston, MA

19h

About The Position

The Director of Data Science will lead the AI Information and Intelligence team within the Global AI Products and Transformation organization in GRS Data Science Solutions. The team develops reusable AI capabilities that help underwriting and claims systems find, extract, and connect information. Its areas include intelligent data and document extraction, external research and enrichment, knowledge, guidelines and policy intelligence, evidence intelligence, and “like-me” comparison capabilities. The role leads a team of data science managers, data scientists, and AI/ML engineers and is accountable for establishing a durable, reusable capability layer that can be extended across business segments and geographies. The Director sets technical direction, owns the domain roadmap, and works with peer AI leaders, segment data science teams, and Technology to build capabilities once, prove them in a lead segment, and scale them across the enterprise. The Director also establishes shared evaluation, quality, feedback, traceability, and audit standards; sets AI development lifecycle and MLOps/AIOps engineering standards; manages team structure and resources; and communicates roadmaps, trade-offs, progress, and recommendations to technical, business, and senior leadership audiences.

Requirements

Demonstrated experience leading data science or AI/ML teams, including direct people management of managers and/or senior individual contributors.
Hands-on experience developing and deploying production-grade AI or ML solutions, with advanced knowledge of AI/ML methods, engineering practices, and tools.
Experience owning a product or capability roadmap and making prioritization trade-offs across multiple stakeholder groups.
Strong communication, collaboration, and stakeholder-management skills, including partnership with peer leaders, engineering/architecture teams, and business sponsors.
Deep knowledge of predictive analytic techniques and statistical diagnostics of models.
Expert knowledge of predictive toolsets, including highly technical tools.
Ability to convey complex information clearly and concisely verbally, visually, and in writing, tailored to audiences and stakeholders.
Broad knowledge of business drivers and market context, with a value-driven understanding of work context and impact.
Relevant experience and education are described as typically acquired through a Ph.D. in a scientific field and at least 6 years of relevant experience including people leadership; a Master’s degree in a scientific field and at least 9 years; or a Bachelor’s degree in a scientific field and at least 11 years.
An additional qualification description states competencies are typically acquired through an advanced degree in Statistics, Mathematics, Data Science, or another relevant field and 5 to 7 years of relevant experience, or a Bachelor’s degree in a scientific field with 10 years of relevant experience.

Nice To Haves

Relevant insurance or regulated-industry experience.
Prior formal management of managers, or experience leading a team of team leads, within data science, AI/ML, or engineering.
Experience leading AI products in a product-oriented delivery model from opportunity identification through scaled adoption.
Experience with GenAI, LLM-based applications, retrieval-augmented generation, agentic workflows, or production-scale AI-assisted document/data extraction.
Experience building reusable AI platforms, shared services, or enterprise-scale capabilities successfully extended across multiple business lines or geographies.
Success driving AI transformation and adoption in large, complex, or global organizations.
Insurance or financial services industry experience.

Responsibilities

Lead, coach, and develop data science managers, data scientists, and AI/ML engineers responsible for the Information and Intelligence capability domain.
Own and prioritize the roadmap for reusable data extraction, external research and enrichment, knowledge/guidelines/forms intelligence, evidence intelligence, and comparable (“like-me”) intelligence capabilities.
Direct the design, development, deployment, and ongoing improvement of production-grade AI/ML solutions, proving them in a lead segment and designing them for extension to other segments and geographies.
Coordinate with AI Products and Decisioning and AI Product Strategy and Scale leaders to sequence shared or dependent capabilities and hand off mature capabilities for adoption.
Partner with segment data science teams and Technology leadership to scale lighthouse solutions into enterprise platforms and define architecture for governed access to relevant data and context.
Establish common evaluation, quality, and feedback standards and ensure outputs are traceable to source and auditable.
Set AI development lifecycle engineering standards covering testing, version control, CI/CD, observability, documentation, and MLOps/AIOps.
Manage team structure, staffing, and workload across onshore, near-shore, and partner resources, including hiring and performance management for direct reports who manage teams.
Communicate roadmap, trade-offs, progress, and recommendations to technical teams, business stakeholders, and senior leadership.
Lead projects and assignments to completion through others, manage talent effectively, and present complex information to management, senior leaders, or executives.
Establish relationships within and outside the aligned functional area or SBU and provide effective training and presentations to management and other groups.