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.
About the Team
The quant team is responsible for making sense of the terabytes of weather data Arbol has at its disposal. It forms the connective tissue between more client-facing teams, such as sales, and back-end roles like data engineering. You’ll be joining a small team of data scientists, engineers and meteorologists and will have a unique opportunity to impact many levels of the firm, such as pricing and product development. This is an ideal position for someone interested in building machine learning systems for climate data while taking a deep dive into the parametric insurance industry.
About the Role
In this role, you will research and implement machine learning techniques for modeling climate data. In addition to analyzing traditional variables such as temperature and precipitation, you will work with alternative data sources like radar and satellite imagery to improve existing products and develop new ones. This will require exciting technical insights coupled with business understanding gained through interaction with other teams.
We are looking for someone with a quantitative background and an interest in applying that skillset toward business-driven research problems at the intersection of climate science and machine learning.
