Staff Data Scientist (Insurance Risk and Pricing)

Role overview Set technical direction for homeowners insurance pricing and risk models, taking complex work from business problem through production deployment. The role centers on generalized linear models while spanning profitability, retention, geospatial analysis, and emerging methods; you’ll partner with actuarial, product, and engineering leaders to turn property data into useful underwriting decisions.

Responsibilities - Establish modeling standards and technical practices for insurance pricing and risk work. - Build and deploy GLMs for claim frequency, severity, and expected loss costs. - Develop machine learning models, including gradient boosted trees and neural networks, to support underwriting decisions. - Own ambiguous modeling problems end to end, from strategy and analysis to production use. - Work with actuarial, product, and engineering partners to apply property data to pricing. - Mentor data scientists and provide technical guidance across the team.

Requirements - Substantial experience in data science, with expertise in insurance pricing or risk modeling. - Strong knowledge of GLM based pricing for homeowners insurance. - Ability to develop and deploy machine learning models in applied settings. - Experience taking complex analytical work into production and connecting it to business outcomes. - Ability to shape technical direction and mentor other practitioners.

Nice to have - Experience with profitability or customer retention models. - Geospatial risk analysis experience. - Familiarity with generative AI techniques.

Benefits and work setup - Remote role based in the United States.