Pay
$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Austin, Texas, United States; Dallas, Texas, United States; Morristown, New Jersey, United States; San Jose, California, United States
Live, first seen by us 34 days agoExperience 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:Read the full posting
Title: Insurance Data Scientist Location: San Francisco Bay Area, CA / Austin, TX / Dallas, TX / Morristown, NJ (Flexible / Hybrid) Reporting to: Sr. Manager, Actuarial Data ScienceRead the full posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
We’re hiring an Insurance Data Scientist to lead loss cost modeling for Hippo’s Homeowners program.
Own the design and annual build of by-peril loss models
Implement and scale models in the Airflow-based Python modeling pipeline
Build reusable analytical tooling and workflows for the actuarial team
Lead homeowners modeling, setting priorities and technical standards
Expand modeling into conversion, retention and aggregation
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.
Own the design and annual build of our by-peril loss models
Implement and scale models within our Airflow-based Python modeling pipeline
Develop reusable analytical tooling and workflows that improve efficiency, consistency, and scalability across the actuarial team
Lead homeowners insurance modeling efforts, setting clear priorities, technical standards, and best practices
Expand modeling capabilities across conversion, retention, and aggregation
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
- 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)
From the employer’s posting.
Hippo was built on a promise: make homeownership effortless.
In the employer’s words · Read in context
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$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Day 34
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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:
- 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
- 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
- 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
- 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
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We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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:
Must Haves:
Nice to Haves:
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:
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.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
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:
Austin, Texas, United States; Dallas, Texas, United States; Morristown, New Jersey, United States; San Jose, California, United States
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
No clear work-authorization passage found. Eligibility is unconfirmed.