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.
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
We are looking for a best-in-class remote sensing scientist to join our research and development team. You should apply if you are eager to employ your top notch geospatial talents and coding skills toward designing new tools to reduce the impact of catastrophic flooding in low and middle-income countries. You will help unlock satellite data to build new types of financial protection and insurance. You will work with a team of scientists and engineers with expertise in remote sensing (optical, radar, and passive microwave), 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. If you are committed to building an innovative and sustainable organization designed to reduce scientific barriers to flood information, this job is for you!
Skills and responsibilities
- Incorporate new flood detection methods at the forefront of science/technology for improving flood data extraction from satellite imagery
- Work with a team of scientists to translate scientific advances from remote sensing, computer vision, and machine learning into useful products by end-users
- Identify scientific advances in the literature and translate ideas into figures, code, and real world impact with our deployment team working with end-users
- Work with the Chief Science Officer and R&D lab to conceive, design and test proof of concepts for humanitarian and insurance applications
- 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.
- This job is remote friendly.
- MS or BS with 3 years of industry experience in geography, earth science, atmospheric science, engineering, computer science, or a related field with a focus on remote sensing and/or geospatial analysis
- Experience working with a variety of satellite data types (optical, SAR, high-resolution)
- Self-starter with ability to work within a fast-paced and rapid-evolving startup
- Proven ability to wrangle data and condense it into meaningful insights through figures
- Prioritizes justice, diversity, science, and solidarity with vulnerable communities
- Understanding of climatology, hydrology, machine learning, or crowdsourced data science methods and techniques
- Experience contributing to a shared codebase on GitHub with multiple collaborators
- Experience using virtual machines on Google Cloud or similar platform
- Experience working with geospatial databases (e.g. PostgreSQL)
- Experience working in disaster relief or in low or middle-income countries