AI technology, Climate Risk modeling, Computational Algorithms, Environmental, Flood analytics, Geoscience, Machine learning techniques, Predictive analytics, Predictive Modeling, Python Programming, R Programming, SQL, Statistical modeling
We are looking for a climate data scientist with experience running data-driven (e.g. machine learning) or computational models. In this role, you will use satellite data, in-situ data and predictive modeling for actionable insights that can be used to ameliorate the impact of catastrophic flooding. You should apply if you are eager to develop scalable approaches to transfer disaster risk to insurance and capital markets and if you are excited to build an innovative and sustainable organization. You will work with a team of scientists and engineers with expertise in optical and microwave remote sensing, hydrology, climate, social vulnerability, and machine learning to build the next generation of tools to ensure financial protection from floods in marginalized communities.
Who you are
Responsibilities
This position is based out of our New York City office but is currently remote. Permanent remote work is possible for the right candidate.
To Apply
Full-Time Team Benefits
Cloud to Street is devoted to building an inclusive and diverse company. Black, Indigenous, and people of color; women, queer people, and all gender identities, and individuals with disabilities are especially encouraged to apply.
Cloud to Street is a top climate tech start-up and the world’s leading remote flood analytics platform. We use global satellites, AI, models, and community intelligence to monitor flood risk and flooding in real-time. Seeded by Google, our paradigm-shifting science made the cover of Nature and is the base for our property technology. Today, governments across almost 20 countries have used our platform for disaster relief.