Remote Lead Data Scientist, Claims & Service

United States Digital Space LLC; United States; Remote; USD 130,000 - 185,000; Full time

Job summary

Liberty Mutual's Claims Data Science team develops AI/ML solutions to enhance the claims process. This role focuses on end-to-end predictive modeling, state-of-the-art ML systems, and collaboration with stakeholders across business lines.

Hybrid work is available for select locations; remote otherwise, with occasional travel. Candidates should have advanced degrees in quantitative fields, strong Python and SQL skills, and experience with MLOps and model deployment.

Qualifications

• Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
• Expert knowledge of predictive toolset; acts as a resource for tool development.
• Ability to convey complex information clearly to peers and leaders.

Responsibilities

• Apply analytics to large structured and unstructured datasets to inform business decisions.
• Lead end-to-end development of predictive models for high-impact outcomes, including data labeling and feature engineering.
• Build ML systems that leverage structured data, unstructured text, and generative AI.
• Select and implement algorithms and evaluation methods to deliver business value.
• Follow ML Ops best practices for reproducible results and production-quality code.
• Mentor junior data scientists and contribute to cross-functional groups.
• Communicate findings through technical reports and presentations.

Skills

SQL

Python

Statistical inference

Stakeholder communication

ML model development

Education

Ph.D. in Statistics/Mathematics/Economics/Actuarial Science or related

Master's degree in a scientific field

Bachelor's degree + 5+ years of experience

Tools

MLflow

Git

GitHub/GitLab

MLOps practices

Job description

Liberty Mutual's Claims Data Science team develops AI/ML solutions to enhance the claims process. This role focuses on end-to-end predictive modeling, state-of-the-art ML systems, and collaboration with stakeholders across business lines.

Hybrid work is available for select locations; remote otherwise, with occasional travel. Candidates should have advanced degrees in quantitative fields, strong Python and SQL skills, and experience with MLOps and model deployment.