Actuarial Data Scientist

Title: Actuarial Data Scientist

Location: San Francisco Bay Area, CA / Austin, TX / Dallas, TX / Morristown, NJ (Flexible / Hybrid)

Reporting to: Director, Actuarial

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 exists to protect the joy of homeownership. We believe that insurance should protect the things you treasure through an intuitive, modern experience. We provide tailored insurance coverage and preventative maintenance plans that keep you protected throughout your homeowner journey. We’ll also help you find coverage for everything life brings—from auto to flood—reimagining how you care for your home.

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 the 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, enjoy building scalable work, and take pride in developing people on your team. 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 a homeowners insurance modeling, 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, data science, or another quantitative field
• 5+ years of experience in data science, analytics, or actuarial modeling within personal lines P&C insurance
• Strong experience with loss cost modeling; exposure to demand, underwriting, and/or claims modeling;
• Knowledge of actuarial principles as they relate to insurance pricing
• Advanced proficiency in Python 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., AWS) and workflow orchestration tools (e.g., Airflow)

The base pay range for this role is $145,000- $160,000 for our Texas (Austin and Dallas regions). The SF Bay Area (and New Jersey) base pay range for this role is $155,000- $180,000. Exact compensation may vary based on seve