Junior Quantitative Researcher

Arbol seeks a Junior Quantitative Researcher in Risk on the Data Science team.
The role is heavily research focused. You will be exploring various experience-driven hypotheses and implementing solutions to test them. Most of the work will be done in the weather-related space, but experience in meteorology is not expected. You will leverage machine learning and deep learning tools to build models that will help us better estimate risk. 
You will also be learning new tools for modeling, reading recent papers and implementing cutting edge approaches, code for which may or may not be available.

What You'll Need

• 1+ years of experience with ML/DL
• 1+ years with native Python
• 1+ years with ML Frameworks (Tensorflow)
• 1+ years with numpy, pandas, jupyter, one of the python DL frameworks (tensorflow is preferred)

What's Great to Have

• Familiarity with matplotlib, seaborn, xgboost, shap, different DL architectures is a plus
• Familiarity with Github

Arbol is a financial services company that helps business entities protect against climate risk. Our mandate is to modernize and grow the climate and weather risk spaces in all directions. Climate data, advanced climate modeling and pricing, blockchain, and open source/community projects are how we use technology to cater to risk-exposed entities in agriculture, energy, and other sectors.
We are based in NYC, but about 70% of the company is fully remote. We offer very good health plans fully covered by the company, as well as stock options. Our salary bands are as follows.