Cloud to Street is the leading flood mapping platform designed to protect the world’s most climate-vulnerable communities. By harnessing global satellites, advanced science, and community intelligence, we monitor worldwide floods in near real-time and remotely analyze local flood exposure at a click of a button. Our mission is to ensure that all vulnerable governments finally access the high quality information they need to prepare for and respond to increasing catastrophes. Founded by two women at Yale and seeded by Google, Cloud to Street is or has been used by governments across 15 countries. We are on track to enable new flood protection and insurance for 10 million people in the next 5 years.
We are looking for a best-in-class remote sensing scientist with radar expertise to lead algorithm development for flood detection with high-resolution imaging radar and passive microwave sensors. You should apply if you are eager to use science to reduce the impact of catastrophic flooding and build an innovative and sustainable organization. In this role, you will lead tasking, testing, and development of high resolution radar algorithms and passive microwave radar algorithms to map flood events. You will work with a team of scientists and engineers with expertise in remote sensing (optical and radar), hydrology, climate, social vulnerability, UX, and machine learning to i) optimize and improve Cloud to Street’s current flood mapping system and ii) build the next generation of tools to ensure financial protection from floods in marginalized communities.
- Lead development of an automated satellite tasking system with climate and weather data
- Develop new and incorporate existing flood detection methods at the forefront of science and technology
- Improve Cloud to Street’s existing algorithms to extract data from commercial radar and passive microwave satellites
- Develop code in python, and manage databases and data assets in Google Cloud Storage, Google Earth Engine Assets, and GitHub
- Collaborate with a team of exceptional scientists and engineers that want you to grow and be successful
- You tell us! Each member has skills not in their job description that are important for our growth. We would love to hear your unique talents and how we can help each other grow.
Characteristics of a Successful Candidate
- Master’s or PhD in geography, earth science, atmospheric science, engineering, computer science, or a related field with a focus on remote sensing and/or geospatial analysis
- Scientifically sound approach to radar remote sensing
- Code proficiency (preferably in python)
- Self-starter with ability to work within a fast-paced and rapid-evolving startup
- Eagerness to learn new skills and help with the task at hand
- Prioritizes justice, diversity, science, and solidarity with vulnerable communities
- Experience using machine learning for data fusion
- Experience with InSAR/Interferometry and passive microwave sensors
- Experience working with climate, weather forecasting, or other to predict extreme events
- Understanding of hydrology and physically-based flood models
- Contributing to a shared codebase on GitHub with multiple collaborators
- Working in disaster relief or in low or middle-income countries
As a Cloud to Street team member, you:
- Lead development of rigorous science at start-up technology company focused on social impact and represent our organization at scientific and development meetings
- Serve the underserved by reducing the scientific barriers for low and middle income countries to access the information governments, businesses, and communities need to sustainably develop and thrive
- Are in solidarity with vulnerable communities by spending time with flood affected populations and organizations who serve them
- Increase equity by making information accessible to historically marginalized communities and building a diverse and inclusive start-up
Applicants are requested to send their submissions to [email protected] with:
- Subject line: Senior Remote Sensing Scientist, Cloud to Street
- Attached CV/resume
- Paragraph expressing interest
- Relevant publications or past projects
Applications will be accepted until the position is filled.