Assistant Director – Claims Data Science

Job Description

Assistant Director, Data Science: Claims & Service | Liberty Mutual

The Tone:
This is a full-time role at Liberty Mutual, with a hybrid schedule for candidates within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX, otherwise it is remote with occasional travel. The Insights & Solutions group uses data, analytics, and technology to deliver innovative solutions for US Retail Markets. Within this group, the Claims Data Science team develops sophisticated AI/ML solutions to create the most accurate, caring, and efficient claims organization. This role is crucial in building a key competitive advantage for Liberty Mutual by leveraging new technologies to solve complex business challenges in claims.

The TL;DR
• Role: Early Career
• Type: Full-time
• Location: Hybrid (within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX) or Remote (otherwise)

• Team: Claims Data Science team within the Insights & Solutions group
• Mission: Develop and deploy AI/ML-driven solutions to enhance claims handling efficiency and accuracy, leveraging advanced technologies like LLMs and Computer Vision.
• Tech Stack: SQL, Python, Git, GitHub/GitLab, MLflow, AWS, Google Cloud, Azure, Airflow

What You’ll Actually Do
• Analytics: Generate insights from large structured and unstructured datasets to inform business decisions.
• Model Development: Lead the complete end-to-end development of new predictive models for high-impact business outcomes, from hypothesis framing to validation.
• System Building: Construct state-of-the-art machine learning systems that leverage structured data, unstructured text, and generative AI.
• MLOps Implementation: Ensure adherence to MLOps best practices for creating organized code repositories, production-quality code, and reproducible results.
• Mentorship: Provide technical guidance and mentorship to junior data scientists within the team.

The Must-Haves
• Background: Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science, or other scientific field) and a minimum of 2 years of relevant experience, a Master`s degree (scientific field) and a minimum of 4 years of relevant experience, or a Bachelor`s degree (scientific field) and a minimum of 5+ years of relevant experience. This includes broad knowledge of predictive analytic techniques and statistical diagnostics of models.
• Experience: Demonstrated expertise in the end-to-end data science lifecycle, including hands-on technical skills. Experience collaborating with non-technical stakeholders to translate business problems into solutions and bring them to market. Proven ability to work with complex Type II data for operational process modeling. Proficiency in MLOps practices, including version control, code review, collaborative development workflows, and model versioning/experiment tracking.
• Skills: SQL, Python, Statistical Inference, Predictive analytic techniques, Statistical diagnostics, MLOps (Git, GitHub/GitLab, MLflow).
• Bonus: Knowledge of claims handling processes or claims data. Experience developing LLM-based solutions for production use cases. Practical experience with cloud platforms like AWS, Google Cloud, or Azure. Familiarity with data pipeline and workflow management tools such as Airflow.