We’re committed to putting people first – if you need an accommodation as you go through the job searching, application, and interviewing process, please click here for more information.
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Massachusetts, United States", "locations": ["Boston, Massachusetts, United States", "Remote, Remote, United States"], "hot": 0, "department": "Data Science", "business_unit": "0116-GRS-Actuarial-US", "t_update": 1793727420, "t_create": 1793634420, "ats_job_id": "262456", "display_job_id": "2026-262456", "type": "ATS", "id_locale": "2026-262456-en", "job_description": "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 \u201cfind / extract / connect\u201d capabilities \u2014 data extraction, external research and enrichment, knowledge/guidelines/forms intelligence, evidence intelligence, and comparable (\u201clike-me\u201d) intelligence \u2014 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 \u2014 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 \u2014 including people leadership \u2014 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.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "remote_local", "canonicalPositionUrl": "https://searchjobs.libertymutualgroup.com/careers/job/618520599876", "isPrivate": false, "latlongs": "42.3555076,-71.0565364"}, {"id": 618520683128, "name": "Director I, Data Science- AI Products", "posting_name": "Director I, Data Science- AI Products", "location": "Seattle, Washington, United States", "locations": ["Seattle, Washington, United States", "Boston, Massachusetts, United States"], "hot": 0, "department": "Data Science", "business_unit": "0116-GRS-Actuarial-US", "t_update": 1794261720, "t_create": 1793915760, "ats_job_id": "262528", "display_job_id": "2026-262528", "type": "ATS", "id_locale": "2026-262528-en", "job_description": "Design, build, and evaluate GenAI solutions and agentic workflow automation systems Build evaluation metrics and tools to assess GenAI systems' performance, reliability, and safety Identify core reusable capabilities within a particular use case and work with other teams to scale those capabilities across other franchises in the business Collaborate with data scientists, AI & data engineers, and business partners to develop AI-enhanced decision-aid tools and drive strategic decision-making Communicate complex technical findings clearly to non-technical stakeholders Serve as a technical consultant on complex, high-impact projects Recruit, manage and develop a team of data scientists and analysts Strong foundational knowledge of data science and AI, combined with statistical knowledge for model diagnosis Proven expertise in building agentic AI systems using LangChain/LangGraph for underwriting automation Strong MLOps expertise across the complete data science lifecycle using MLFlow3/Databricks Familiarity with GenAI evaluation methodologies and experience assessing AI workflows Demonstrated ability to exchange ideas and convey complex information clearly and concisely Ability to guide and mentor junior data scientists and analysts on the team Proven ability to build and sustain relationships both within and beyond the department Ability to translate analytical results into recommendations that influence team, department, and senior leadership decisions Broad knowledge of the business and market context, in particular, commercial underwriting & claims workflows, and ability to prioritize work based on the value it generates. Competencies typically acquired through a Ph.D. in Statistics, Mathematics, Economics, or another scientific field, plus a minimum of 3 years of relevant experience, a Master's degree (scientific field of study) and a minimum of 6 years of relevant experience; or may be acquired through a Bachelor's degree (scientific field of study) and a minimum of 8 years of relevant experience. Responsible for the management, technical oversight and development for a team of data science and advanced analytics staff. Collaborates with business partners to develop tools and services such as modeling, dashboard development, and decision aids to drive strategic decision making. Presents findings, shares insights, and makes recommendations that impact profitability, growth and/or customer satisfaction. Provides tactical input on highly technically complex projects that drive change across function, SBU or Corporate Department. Supervises and performs highly complex well-defined work, containing technical and creative predictive analytics projects. 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Design, build, and iterate on Machine Learning and Generative & Agentic AI capabilities which deliver real business value for adjusters in Medical and Workers' Compensation claims. Collaborate with our partner teams to architect & deliver end-to-end intelligent systems integrating machine learning, LLMs, RAG pipelines, agentic workflows, and structured/unstructured claims data to solve real business problems. Apply strong software engineering and ML/AI Ops practices, including object-oriented Python, modular design, test-driven development, and model monitoring. Define and implement evaluation and measurement strategies, including impact & acceptance KPIs, human-in-the-loop review, and automated quality metrics. Collaborate with Engineers, Product, Architecture, UX, and Claims stakeholders to translate ambiguous problems into working solutions. Provide technical mentorship and thought leadership through design reviews and collaboration. Expert-level machine learning & statistical modeling practitioner with track record for delivering measurable business value. Deep knowledge of insurance claims processes, especially in Workers' Compensation or other medical-driven lines of business (e.g., Medical & Casualty, Disability). Experienced in Python development with object-oriented design and engineering best practices. Demonstrated AI/ML Ops experience including model lifecycle management, experimentation, evaluation, monitoring, and operational readiness in a cloud-native enterprise environment. Demonstrated ability to measure AI/ML and GenAI system quality and business impact using quantitative and qualitative approaches. Experience partnering with technical and business stakeholders in complex, enterprise environments. Experience delivering ML and AI products through Python API and Application frameworks like FastAPI and Streamlit (or equivalent) Demonstrated experience delivering business value with Generative & Agentic AI in an enterprise setting Building, deploying, and maintaining AI products using leading frontier models from providers like OpenAI, Anthropic, and Google Experience building generative & agentic AI solutions using frameworks like LlamaIndex, LangChain, LangGraph, or equivalent Exposure to CrewAI, AgentCore, or other agentic orchestration frameworks Familiarity with vector databases and embedding-based retrieval systems Deep knowledge of predictive analytic techniques and statistical diagnostics of models. Expert knowledge of predictive toolset. Demonstrated ability to exchange ideas and convey complex information clearly and concisely. Ability to establish and build relationships within and outside the organization. Ability to give effective training and presentations to management, executives and other groups. Ability to use results of analysis to persuade team, department management or senior management to a particular course of action. Deep knowledge of business drivers and market context. Has a value driven perspective with regard to understanding of work context and impact. 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