06/04/2026

Manager, Actuarial Data Science

Job Description

Title: Manager, Actuarial Data Science

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 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 are seeking a Manager to lead an insurance modeling team responsible for supporting Hippo’s owned Homeowners program. This role will be responsible for developing modeling capabilities that support pricing, underwriting, and growth, with a strong focus on loss modeling, demand modeling, and weather risk. This role will also develop advanced Python and SQL based tooling that packages core pricing and expected loss ratio analytics into scalable, reusable workflows, significantly improving the efficiency and consistency of segmentation analyses.

You’ll be responsible not only for building high-quality models, but also for seeing their use through to completion in both offline and production systems as appropriate. This role works closely with Pricing, Underwriting, Claims, Product, and Data Engineering, and is ideal for someone who is equally comfortable diving into technical details as they are communicating clearly with non-technical stakeholders.

About You:

You are a hands-on data science 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:

  • Build and lead a homeowners insurance modeling team, 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
  • 7+ years of experience in data science, analytics, or actuarial modeling within personal lines P&C insurance
  • 3+ years of people management experience, including coaching and performance development
  • 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
  • Experience using Git in collaborative environments
  • 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 modeling catastrophe or weather-driven losses

The SF Bay Area (and New Jersey) base pay range for this role is $185,000- $245,000. Exact compensation may vary based on several job-related factors that are unique to each candidate, including but not limited to: skill set, experience, education/training, location, business needs and market demands. 

Benefits and Perks:

Hippo treats its team members with the same level of dedication and care as we do our customers, which is why we’re fortunate to provide all of our Hippos with: 

  • Relocation Assistance - Support available for qualified candidates relocating to one of our hub locations
  • Healthy Hippos Benefits - Multiple medical plans to choose from and 100% employer covered dental & vision plans for our team members and their families. We also offer a 401(k)-retirement plan, short & long-term disability, employer-paid life insurance, Flexible Spending Accounts (FSA) for health and dependent care, and an Employee Assistance Program (EAP) 
  • Equity - This position is eligible for equity compensation  
  • Training and Career Growth - Training and internal career growth opportunities 
  • Flexible Time Off - You know when and how you should recharge 
  • Little Hippos Program - We offer 12 weeks of parental leave for primary and secondary caregivers 
  • Hippo Habitat - Snacks and drinks available and catered lunches for onsite employees 

Hippo is an equal opportunity employer, and we are committed to building a team culture that celebrates diversity and inclusion.  

Hippo’s applicants are considered solely based on their qualifications, without regard to an applicant’s disability or need for accommodation. Any Hippo applicant who requires reasonable accommodations during the application process should contact the Hippo’s People Team to make the need for an accommodation known. 


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