Director, Data Science: Medical, Special Investigations, and Programs (MSP) Claims Data Science team

Liberty Mutual Insurance•Boston, MA

15d•$137,000 - $281,000•Hybrid

About The Position

The Medical, Special Investigations, and Programs (MSP) Claims Data Science team is hiring a Director, Data Science to support business demand in Medical and Workers’ Compensation Claims. This is a senior individual-contributor role for an experienced, execution-focused data scientist who will independently partner with stakeholders to identify, prioritize, and build machine learning and AI capabilities in an area with a minimal existing data science footprint. The role will partner with Claims leaders and delivery partners in Insights & Solutions and Technology to establish and execute a roadmap of foundational Machine Learning and AI capabilities that enhance adjuster workflows and related processes, such as Quality Improvement. The role is expected to build a team as the execution strategy is established. The position may have in-office requirements depending on the candidate’s location. Candidates living within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX follow a hybrid schedule and work from the office two days per week. Otherwise, the role is remote with occasional travel.

Requirements

Expert-level machine learning and statistical modeling expertise, with a track record of delivering measurable business value.
Deep knowledge of insurance claims processes, especially Workers’ Compensation or other medical-driven lines of business such as Medical & Casualty or Disability.
Experience developing in Python using 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.
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.
Deep knowledge of predictive analytic techniques and statistical diagnostics of models.
Expert knowledge of predictive tools and ability to exchange ideas and convey complex information clearly and concisely.
Ability to establish and build relationships within and outside the organization.
Ability to provide effective training and presentations to management, executives, and other groups.
Ability to use analysis results to persuade teams and management to a course of action.
Deep knowledge of business drivers and market context, with a value-driven perspective on work context and impact.
Competencies typically acquired through a Ph.D. in Statistics, Mathematics, Economics, Actuarial Science, or another scientific field and 5 years of relevant experience; a master’s degree in a scientific field and 8 years of relevant experience; or a bachelor’s degree in a scientific field and 12 years of relevant experience.

Nice To Haves

Experience delivering ML and AI products through Python API and application frameworks such as FastAPI and Streamlit, or equivalent.
Demonstrated experience delivering business value with Generative & Agentic AI in an enterprise setting.
Experience building, deploying, and maintaining AI products using frontier models from providers such as OpenAI, Anthropic, and Google.
Experience building generative and agentic AI solutions using frameworks such as LlamaIndex, LangChain, LangGraph, or equivalent.
Exposure to CrewAI, AgentCore, or other agentic orchestration frameworks.
Familiarity with vector databases and embedding-based retrieval systems.

Responsibilities

Partner with Claims leaders and delivery partners in Insights & Solutions, Technology, and Enterprise Data to identify AI/ML opportunities and create a roadmap prioritizing impactful work.
Design, build, and iterate on Machine Learning and Generative & Agentic AI capabilities that deliver business value for adjusters in Medical and Workers’ Compensation claims.
Collaborate with partner teams to architect and deliver end-to-end intelligent systems integrating machine learning, LLMs, RAG pipelines, agentic workflows, and structured and unstructured claims data.
Apply 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 and 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.
Establish an execution strategy and build a team as the role develops.