Actuarial Analyst – Climate Risk & AI Pricing

Arbol; United States; A distancia; USD 70.000 - 95.000; Jornada completa

Descripción de la vacante

Arbol is a global climate risk coverage platform and FinTech seeking an actuarial analyst to support pricing, reserving, reinsurance analytics, and portfolio management on homeowners policies in coastal southern states. You will translate complex actuarial processes into scalable, automated workflows in collaboration with AI and engineering teams.

The role emphasizes ownership of analyses with sometimes imperfect data, building robust Python-based tools, and delivering insights that inform

Formación

• Bachelor’s degree in Actuarial Science, Mathematics, Statistics, or related quantitative field.
• 1–4 years of P&C actuarial experience (homeowners preferred).
• Proficiency in Python and Excel.
• Ability to communicate findings clearly to non-technical stakeholders.

Responsabilidades

• Pricing & Rate Indications: Develop rate indications and pricing analyses (territory, deductible, rating factors).
• Reserving & Loss Analytics: Perform quarterly reserve analyses and monitor loss trends.
• Reinsurance Analytics & Reporting: Support reinsurance reporting and analyze program impact.
• Actuarial Process Automation & AI Partnerships: Write Python-based workflows and translate actuarial logic for automation.
• Portfolio Management & Performance Monitoring: Build dashboards and analyze portfolio performance.

Conocimientos

Python

Excel

P&C actuarial

Educación

Bachelor's degree in Actuarial Science, Mathematics, Statistics

Herramientas

Python

Descripción del empleo

Arbol is a global climate risk coverage platform and FinTech seeking an actuarial analyst to support pricing, reserving, reinsurance analytics, and portfolio management on homeowners policies in coastal southern states. You will translate complex actuarial processes into scalable, automated workflows in collaboration with AI and engineering teams.

The role emphasizes ownership of analyses with sometimes imperfect data, building robust Python-based tools, and delivering insights that inform