Title: Insurance 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 Insurance 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, Databricks, AWS) and workflow orchestration tools (e.g., Airflow)