Senior Remote Sensing Scientist via AGU

Cloud to Street

New York, NY, US

DESCRIPTION Lead the development of new global algorithms to detect floods from a variety of sensors in Google Earth Engine

LOCATION New York, NY or where office space is provided; remote working is possible for the right candidate

SALARY & COMPENSATION Equity and salary competitive with comparable startups; generous benefits

REPORTS TO Chief of Science, Cloud to Street


Cloud to Street is a remote sensing platform that rapidly maps floods around the world and delivers risk information in a web dashboard at a fraction of the cost and time of traditional modeling. Our mission is to ensure that all vulnerable communities have the data they need to prepare and respond by reducing the scientific barrier to disaster information. Our systems will power flood insurance for 50 million new customers and provide risk data for the 100 most vulnerable watersheds around the world within 5 years.

In partnership with Google Earth Outreach, the Dartmouth Flood Observatory and others over the last 3 years, we developed the core algorithms behind our technology and conducted initial socio-physical risk assessments in India, Argentina, The Nile Basin, Senegal and New York, funded primarily by the World Bank. In 2018, we are releasing the world’s largest database of flood maps, launching the Flood Risk Dashboard product, and expanding to at least three new countries. These projects will involve flood monitoring, developing historical time series to calibrate hydrologic models used by basin managers, and testing the use of satellites to automate microinsurance payouts. This year, we specifically plan to expand our work to urban areas. We are looking for a best-in-class remote sensing scientist to lead algorithm development, especially for urban areas, and direct our remote sensing science. In addition to algorithm development, this person will provide technical support to project stakeholders, help develop project proposals, and assist with online user tool development.



● Build on Cloud to Streets existing flood detection algorithms in Google Earth Engine using the Python API

● Develop of new algorithms to detect floods with public and commercial sensors- including radar, optical, and UAV, with a focus on urban areas

● Develop methods to compare multi-satellite results to ground reference data

● Manage of databases and data assets in Google Cloud Storage, Google Earth Engine Assets, and GitHub

● Technical support for completion of final written reports, training materials


● Masters or PhD (Ph D preferred) in Geosciences or related field with focus on remote sensing and geospatial analysis.

● Experience with data science related field such as computer science, applied mathematics, information technology, physics or software engineering is a plus.

● Proven track record developing new remote sensing methods

● A commitment to justice, diversity, science and solidarity with vulnerable communities

● Ability to work in a team remotely, in a diverse work environment and with an entrepreneurial startup mentality

● Preferred: Experience with Google Earth Engine Javascript and Python APIs

● Preferred: Experience using the Google Cloud Ecosystem

● Preferred: Experience using and combining optical, radar, UAV, and high resolution imagery

● Plus: Experience using machine learning to assess social media and/or other types of crowdsourcing data


● Competitive start-up salary

● Equity stake in the company

● Office space in New York City

● Generous health care package

Cloud to Street is committed to building an inclusive and diverse company. Women, people of color, and individuals with disabilities are especially encouraged to apply.



Applicants are requested to send their submissions to [email protected] with:

1. Subject line: Senior Remote Sensing Scientist, CLOUD TO STREET

2. Relevant publications or other scientific work

3. Attached CV/Resume

4. Paragraph expressing interest within the submission email

APPLICATION DEADLINE: Applications will be reviewed until July 1, with the intent to start the right candidate as soon as possible.