Director, Data Science

Are you looking for a hands-on role at the cutting edge of Generative AI research and solutioning in insurance? Do you want to design and implement science-driven GenAI features spanning multimodal data such as imagery, structured data, text, and audio that can transform pricing, risk evaluation, and product innovation?

The Modeling Sophistication Deep Learning Research team is seeking a highly skilled technical leader who combines strong research depth with practical engineering ability. In this role, you will be both building end-to-end GenAI solutions yourself and providing technical direction, mentorship, and influence to peers and partners. You will explore novel architectures, LLM-based pipelines, retrieval-augmented generation (RAG) applications and evaluation, multimodal fusion, and generative approaches for creating new features, while ensuring seamless integration into Liberty Mutual’s business applications.

This position is ideal for someone with deep curiosity, tenacity, and the drive to scale the impact of GenAI research across diverse insurance products without traditional managerial responsibilities.

**This role may have in-office requirements based on candidate location.**

**Level of position offered will be based on skills and experience at manager discretion*

The successful candidate will:

• Have a proven track record of developing GenAI-driven solutions (e.g., LLMs, multimodal models, diffusion models, foundation models) for complex real-world use cases.
• Be fluent in hands-on development, from data wrangling to model training/fine-tuning, evaluation, and deployment.
• Possess strong expertise in AWS-native tech stack and MLOps best practices.
• Bring practical experience in scaling GenAI models and services for business-facing applications.
• Demonstrate the ability to translate cutting-edge GenAI research into features with measurable business impact.
• Provide technical leadership and guidance, influencing best practices, solution architectures, and research directions across teams.
• Value collaboration with engineers, researchers, and business partners to bring novel science-based ideas to market.

Responsibilities:

• Research, design, and build GenAI-powered feature pipelines, including text, vision, audio, and multimodal integrations.
• Serve as a subject matter expert for GenAI solutioning in insurance use cases (pricing, claims, underwriting, product innovation). Prior Insurance knowledge / experience is not required.
• Manage (or help manage for Asst Dir role) the end-to-end lifecycle of generative models and features, including training, fine-tuning, deployment, monitoring, and CI/CD pipelines.
• Research and apply retrieval-augmented generation (RAG) techniques, including developing pipelines, optimizing retrieval strategies, and evaluating RAG system performance.
• Drive MLOps best practices for scalable deployment of LLMs and other generative models in production environments.
• Mentor peers and junior scientists/engineers through technical guidance and code-level contributions, without direct line management.
• Collaborate with vendors and internal partners to evaluate and integrate external GenAI tools, foundation models, and datasets.
• Partner with the broader research and data science community to advance state-of-the-art methodologies and foster cross-team knowledge sharing.
• Communicate findings and strategic recommendations to leadership, ensuring business and technical alignment.

• Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
• Advanced knowledge of predictive toolset; reflects as expert resource for tool development.
• 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 and other groups.
• Ability to use results of analysis to persuade team, department management or senior management to a particular course of action.
• 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 a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and 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.