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

Partner closely with leaders in Claims and other delivery partners (Insights & Solutions, Technology, and Enterprise Data) to identify AI/ML opportunities and create a delivery roadmap that prioritizes the most impactful work. 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. Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and 5 years of relevant experience, a Master`s degree (scientific field of study) and 8 years of relevant experience or may be acquired through a Bachelor`s degree(scientific field of study) and 12 years of relevant experience.