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, Databricks, AWS) and workflow orchestration tools (e.g., Airflow) Experience with catastrophe modeling, geospatial analytics, or climate risk data 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 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. Applicant & Employee Privacy Notice (CCPA/CPRA)

Requirements

Must-haves

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
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.
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
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

Bachelor’s degree in statistics, mathematics, actuarial science, computer science, or a another quantitative field; advanced degree a plus
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)
Experience with catastrophe modeling, geospatial analytics, or climate risk data

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonsqlaws

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave