Actuarial Data Scientist at Hippo in Austin, TX

Job description

Title: Actuarial Data Scientist Location: San Francisco Bay Area, CA / Austin, TX / Dallas, TX / Morristown, NJ (Flexible / Hybrid) Reporting to: Sr. Manager, Actuarial Data Science Open to Relocation? We’ve Got You Covered: This role is open to candidates willing to relocate to one of our hub locations. Hippo offers relocation assistance to support your move , so you can focus on doing impactful work—not the logistics of getting here. About Hippo: Hippo was built on a promise: make homeownership effortless. Nearly a decade later, that mission still drives us. We use technology and data to help our customers stay ahead of problems and protect what matters most. Today, that same tech-native approach powers our work beyond homeowners. Hippo operates as a diversified carrier platform, partnering with MGAs to deliver tailored program solutions that help them grow and deliver better customer experiences. Behind that work is a team that values ownership, curiosity, collaboration, and continuous improvement. If you're energized by building what's next, we'd love to meet you. About This Role: 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. About You: You are a hands-on leader with a strong background in loss modeling. You bring structure to ambiguity and enjoy building scalable work and collaborating with other teams. You think critically about model maintenance cost and you balance scientific rigor with practical business impact. You communicate clearly, document thoroughly, and operate with a strong sense of ownership. What You’ll Do: 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 Must Haves: 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 Nice to Haves: Actuarial credentials - ACAS/FCAS Master’s degree in a quantitative discipline Experience working in modern data platforms, including familiarity with cloud infrastructure (e.g., Snowflake, Dat