Actuarial Data Scientist, Hippo Insurance

• Bachelor’s degree in statistics, mathematics, actuarial science, computer science, or a another quantitative field; advanced degree a plus
• 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; exposure to demand, underwriting, and/or claims modeling
• Strong background in statistical modeling: GLMs, regularization, tree-based ensembles (XGBoost, LightGBM), and model validation techniques
• Knowledge of actuarial principles as they relate to insurance pricing
• Advanced proficiency in Python (pandas, scikit-learn, statsmodels) and SQL
• Excellent communication skills and ability to build trust with stakeholders at all levels

Desirable

• We’re hiring an actuarial data scientist to lead loss cost modeling for Hippo’s Homeowners program
• This role will own the design, development, and deployment of predictive models for frequency and severity, ensuring strong alignment with pricing strategy and business objectives
• You’ll build and scale Python- and SQL-based tooling that embeds expected loss ratio and segmentation analytics into reusable, production-ready workflows
• In addition to leading loss cost modeling, you’ll have the opportunity to expand the team’s modeling capabilities across demand and aggregation models to inform broader pricing and business mix decisions
• Lead homeowners insurance modeling efforts, setting clear priorities, technical standards, and best practices while fostering a culture of ownership, rigor, and continuous improvement
• Own the design and annual build of our by-peril loss models
• Expand modeling capabilities across conversion, retention, and aggregation to inform pricing strategy, business mix, and portfolio performance
• Implement and scale models within our Airflow-based Python modeling pipeline, ensuring robust testing, validation, reproducibility, and long-term maintainability
• Develop reusable analytical tooling and workflows that improve efficiency, consistency, and scalability across the actuarial team
• Partner cross-functionally with Insurance Product, Underwriting, and Engineering to translate complex modeling insights into clear business recommendations and ensure successful production deployment