Actuarial Data Scientist

What you'll do

• Lead homeowners insurance modeling efforts by setting technical standards and best practices.
• Own the design and annual build of by-peril loss models.
• Expand modeling capabilities across conversion, retention, and aggregation to inform pricing strategy.
• Implement and scale models within an Airflow-based Python modeling pipeline.
• Develop reusable analytical tooling and workflows to improve efficiency across the actuarial team.
• Translate complex modeling insights into clear business recommendations for cross-functional partners.

What we're looking for

• Bachelor's degree in statistics, mathematics, data science, or another quantitative field.
• Master's degree in a quantitative discipline (preferred).
• 5+ years of experience in data science, analytics, or actuarial modeling in insurance, preferably personal lines or property.
• Working knowledge of P&C insurance in loss cost modeling and exposure to demand, underwriting, and/or claims modeling.
• Strong background in statistical modeling including GLMs, regularization, tree-based ensembles, and model validation techniques.
• Knowledge of actuarial principles as they relate to insurance pricing.
• Advanced proficiency in Python (pandas, scikit-learn, statsmodels) and SQL.
• Actuarial credentials such as ACAS/FCAS (preferred); experience with cloud infrastructure, Airflow, catastrophe modeling, or geospatial analytics (preferred).