Quantitative Researcher

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 TeamThe 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 RoleIn 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.

What You'll Be Doing

• Design and implement machine learning approaches for forecasting and generative modeling
• Develop models for climate and weather perils such as heat waves, severe convective storms, and tropical cyclones
• Build robust training and validation pipelines for climate datasets
• Work with risk and insurance teams to perform business-critical analytics

What You'll Need

• BA in statistics, computer science, mathematics, or related quantitative field
• Experience programming in Python
• Experience analyzing large datasets
• Strong problem solving and analytical skills

What's Great to Have

• Graduate degree and/or research experience in a quantitative field
• Comfort with statistics (e.g., linear regression, hypothesis testing)
• Experience working with time series and/or climate data

Candidates for this role must be located in the United States.
Interested, but you don’t meet every qualification? Please apply! Arbol values the perspectives and experience of candidates with non-traditional backgrounds and we encourage you to apply even if you do not meet every requirement.
AccessibilityArbol is committed to accessibility and inclusivity in the hiring process. As part of this commitment, we strive to provide reasonable accommodations for persons with disabilities to enable them to access the hiring process. If you require an accommodation to apply or interview, please contact hr@arbol.io
BenefitsArbol is proud to offer its full-time employees competitive compensation and equity in a high-growth startup.  Our health benefits include comprehensive health, dental, and vision coverage, and an optional flexible spending account (FSA) to support your health.  We offer a 401(k) match to support your future, and flexible PTO for you to relax and recharge.