Director, Data Science

Liberty Mutual Insurance•Boston, MA

16h•$156,000 - $281,000

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, including data and document extraction, external research and enrichment, knowledge and policy intelligence, evidence intelligence, and comparable (“like-me”) capabilities. The Director will build and manage a team of data science managers, data scientists, and AI/ML engineers, and establish a reusable capability layer that can be extended across business segments and geographies. The role sets technical direction and the capability roadmap, and partners 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 position also establishes standards for evaluation, quality, traceability, auditability, and the AI development lifecycle, while managing team structure, staffing, workload, and communication with technical teams, business stakeholders, and senior leadership.

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 stakeholder groups.
Strong communication, collaboration, and stakeholder-management skills, including partnership with peer leaders, engineering and 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 communicate complex information clearly and concisely verbally, visually, and in writing, adapting 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 may be met through a Ph.D. in a scientific field and at least 6 years of relevant experience including people leadership; a scientific Master’s degree and at least 9 years of relevant experience; or a scientific Bachelor’s degree and at least 11 years of relevant experience.
An alternative qualification statement cites an advanced degree in Statistics, Mathematics, Data Science, or another relevant field and 5 to 7 years of relevant experience, or a scientific Bachelor’s degree and 10 years of relevant experience.

Nice To Haves

Relevant insurance or regulated-industry experience.
Prior formal management of managers, or experience leading team leads, in 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 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 the roadmap for reusable information-finding, extraction, and connection capabilities, and prioritize investment for reuse and value.
Direct the design, development, deployment, and improvement of production-grade AI/ML solutions built to extend from a lead segment to other segments and geographies.
Coordinate with peer AI leaders on sequencing shared and 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 data and context access.
Establish common evaluation, quality, and feedback standards, ensuring outputs are traceable to source and auditable.
Set AI development lifecycle engineering standards, including testing, version control, CI/CD, observability, documentation, and MLOps/AIOps.
Manage team structure, staffing, and workload across onshore, near-shore, and partner resources; hire and manage performance of direct reports who manage teams.
Communicate roadmaps, trade-offs, progress, and recommendations to technical teams, business stakeholders, and senior leadership.
Drive completion of projects and assignments through others, manage talent, and present information to senior or functional leaders and executives.
Build relationships within and outside the aligned functional area or SBU, and provide effective training and presentations to management and other groups.