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.
What You’ll Be Doing
In this role, you will research and implement machine learning techniques for modeling climate data. On top of more traditional variables (think temperature or 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 experience in AI engineering (e.g., model development and deployment) and who is interested in growing into a more analytical AI scientist role.
You can expect to:
Design and implement machine learning approaches for forecasting and generative modeling (PyTorch, Tensorflow, Keras, sklearn)
Build robust training and validation pipelines for large, climate datasets (Python)
Work with risk and insurance teams to perform business-critical analytics
About the Team
The Data Science 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 insurance, and back-end roles like data engineering or risk management. 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.
What You'll Need
- BA in computer science, statistics, or related quantitative field
- Experience programming in Python
- Experience with at least one major machine learning framework: e.g., PyTorch, Tensorflow, Keras
- Experience analyzing large datasets
- Strong problem solving and analytical skills
What’s Great to Have
- Software development experience, including coding best practices
- Experience working with time series and/or climate data
- Experience working with computer vision
- Comfort with statistics (e.g., linear regression, hypothesis testing)
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.
Accessibility
Arbol 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 [email protected].
Benefits
Arbol is proud to offer its full-time employees competitive compensation and equity in a high-growth startup. Our health benefits include 99% employer-paid 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 20 days of PTO for you to relax and recharge.
Arbol uses E-Verify to confirm the employment eligibility of all new employees. To learn more about E-Verify, including your rights and responsibilities, please visit www.DHS.gov/E-Verify.
