Director, Data Science

Lead, coach, and develop a team of data science managers, data scientists, and AI/ML engineers responsible for the Information and Intelligence capability domain. Own the roadmap for reusable “find / extract / connect” capabilities — data extraction, external research and enrichment, knowledge/guidelines/forms intelligence, evidence intelligence, and comparable (“like-me”) intelligence — and prioritize investment for maximum reuse and value. Direct the design, development, deployment, and ongoing improvement of production-grade AI/ML solutions that prove out in a lead segment and are engineered from the start to extend to other segments and geographies. Partner with the leaders of AI Products and Decisioning and AI Product Strategy and Scale to sequence shared/dependent capabilities (e.g., hazard/concern flagging, rules and guideline intelligence, information sufficiency detection) and to hand off mature capabilities for adoption. Partner with segment data science teams and Technology leadership to translate lighthouse solutions into enterprise-scale platforms, defining the architecture that gives AI systems governed access to relevant data and context. Establish common evaluation, quality, and feedback standards for the capabilities the team owns, and ensure outputs are traceable to source and auditable. Set engineering standards across the team's AI development lifecycle — 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 themselves manage teams. Communicate roadmap, trade-offs, progress, and recommendations clearly to technical teams, business stakeholders, and senior leadership. Demonstrated experience leading data science or AI/ML teams, including direct people-management of managers and/or senior individual contributors. Hands-on background 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 experience partnering with peer leaders, engineering/architecture teams, and business sponsors. Relevant insurance or regulated-industry experience is preferred. Competencies typically acquired through a Ph.D. degree (in Computer Science, Statistics, Mathematics, Economics, Actuarial Science, or other scientific field of study) and a minimum of 6 years of relevant experience — including people leadership — a Master's degree (scientific field of study) and a minimum of 9 years of relevant experience, or a Bachelor's degree (scientific field of study) and a minimum of 11 years of relevant 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 AI-assisted document/data extraction at production scale. Experience building reusable AI platforms, shared services, or enterprise-scale capabilities that were successfully extended across multiple business lines or geographies. Success driving AI transformation and adoption in large, complex, or global organizations. Deep knowledge of predictive analytic techniques and statistical diagnostics of models. Expert knowledge of predictive toolset, to include highly technical tools. Proven ability to lead and drive projects and assignments to completion through others, to include prior experience effectively managing talent. Demonstrated ability to exchange ideas and convey complex information clearly and concisely, verbally, visually (presentations and exhibits), and in writing, consistent with the audiences and stakeholders with which s/he interacts. Ability to establish and build relationships within and outside the aligned functional area or SBU. Ability to give effective training and presentations to management and other groups. Presents to senior or functional department leaders or executives. Broad knowledge of business drivers and market context. Has a value driven perspective with regard to understanding of work context and impact. Competencies typically acquired through an advanced degree (in Statistics, Mathematics, Data Science or other relevant field of study) and 5 to 7 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) with 10 years of relevant experience.