Arbol is a global climate risk coverage platform and FinTech company offering full-service solutions for any business looking to analyze and mitigate exposure to climate risk. Arbol’s products offer parametric coverage which pays out based on objective data triggers rather than subjective assessment of loss. Arbol’s key differentiator versus traditional InsurTech or climate analytics platforms is the complete ecosystem it has built to address climate risk. This ecosystem includes a massive climate data infrastructure, scalable product development, automated, instant pricing using an artificial intelligence underwriter, blockchain-powered operational efficiencies, and non-traditional risk capacity bringing capital from non-insurance sources. By combining all these factors, Arbol brings scale, transparency, and efficiency to parametric coverage.

We're looking for a talented actuarial analyst to support core actuarial work across pricing, reserving, reinsurance analytics, and portfolio management. You'll work primarily on homeowners policies in coastal southern states, while supporting our growth into new lines and territories. A key part of this role is helping translate complex actuarial processes into scalable, automated workflows by partnering closely with our AI and engineering teams. The ideal candidate takes real ownership of their work, builds clean and rigorous analyses with sometimes imperfect data, and is comfortable working in Python as a core part of their toolkit.

What You'll Be Doing

Pricing & Rate Indications

-Develop rate indications and pricing analyses (e.g., territory, deductible, and other rating factors).

-Monitor rate adequacy, loss trends, inflation impacts, and emerging experience.

-Build and maintain pricing tools, exhibits, and documentation used for internal decisions and regulatory filings.

Reserving & Loss Analytics

-Perform quarterly reserve analyses (e.g., triangles, ultimate loss selections, IBNR, diagnostics) for homeowners and related lines.

-Monitor loss emergence, claim severity/frequency trends, and CAT vs non-CAT performance.

-Partner with Claims and Finance to reconcile results, investigate drivers, and improve data quality.

Reinsurance Analytics & Reporting

-Support reinsurance reporting and analytics (e.g., ceded loss estimates, billing, treaty performance monitoring).

-Help evaluate reinsurance program impacts on earnings volatility, capital, and tail risk.

Actuarial Process Automation & AI Partnershi -Learn Lilypad's actuarial processes end-to-end and identify opportunities for automation (e.g., gross-to-net loss calculations, reserve workflows).

-Serve as a bridge between the actuarial and AI/engineering teams, translating actuarial logic into requirements that can be built into automated tools.

-Write and maintain Python-based workflows to streamline recurring analyses; collaborate with the AI team on tool development.

Portfolio Management & Performance Monitoring

-Build recurring dashboards and deep-dives on portfolio performance: loss ratio decomposition, retention, mix shift, underwriting actions, and segment performance.

-Support scenario analyses (e.g., catastrophe load sensitivity, trend sensitivity, rate change impacts).

Cross-Functional Support

-Collaborate with Underwriting, Claims, Finance, Data/Engineering, and Leadership on special projects (product changes, underwriting guidelines, new territories/segments, vendor evaluation, etc.).

-Contribute to process improvements, automation, and better controls/documentation.

What You’ll Need

-Bachelor’s degree in Actuarial Science, Mathematics, Statistics, or related quantitative field

-Proficiency in Python & Excel

-1-4 years of P&C actuarial experience (homeowners preferred).Progress towards CAS credentials: at least 2 exams passed; more is a plus.
-Strong analytical and problem-solving skills; able to communicate findings clearly to non-technical stakeholders.

What's Great to Have

-Homeowners experience: catastrophe exposure, weather/CAT analytics, inflation/trend work, or rate filing support.

-Experience with reserving methods (e.g., chain ladder/BF/Cape Cod style approaches) and/or pricing indications.

-Familiarity with reinsurance structures (quota share, XoL, cat programs) and basic ceded reporting concepts.

-Experience building repeatable analytics (dashboards, automated data pulls, version-controlled workflows).

-Knowledge of policy/claims data structures and common insurance KPIs.