Radar Remote Sensing Scientist

Radar Remote Sensing Scientist

About the company

Cloud to Street is the recognized authority on using satellites and AI to track floods anywhere on earth. Founded by two climate experts with initial funding from Google, the team comes from NASA, Facebook, Google, Willis Towers Watson, and Oscar Insurance. Our platform has been used by 17 countries to fill critical information gaps for disaster planning and response, and is the official emergency flood mapping provider to the United Nations. The technology is now enabling insurers to expand protection to billions of uninsured assets and people through partnerships with Munich RE and Willis Towers Watson. We recently raised a large seed from top investors that will be announced soon.

Role

We are looking for a best-in-class remote sensing scientist with expertise in analysis of high-resolution synthetic aperture radar (SAR) imagery to lead algorithm development for flood detection. You will work closely with our team’s remote sensing scientists, machine learning engineers, and hydrologists to build novel flood analytics and practical decision-support tools for disaster responders, flood managers, and insurers. You should be able to creatively integrate high resolution SAR observations with auxiliary datasets to tackle challenging problems, such as making flood maps in urban areas.

Who You Are

Masters or PhD in geosciences, engineering, or related field with focus on remote sensing and geospatial analysis, or 2+ years relevant work experience in industry focused on SAR remote sensing

Fundamental knowledge of active microwave remote sensing, including multi-temporal analysis, and working with both intensity and phase components of microwave backscatter

Proven track record of developing new remote sensing methods

Commitment to justice, diversity, science and solidarity with vulnerable communities
Experience with data fusion/assimilation approaches using observations from multiple sensors or disparate data types, especially approaches based on machine learning

Responsibilities

Lead integration of SAR imagery based flood mapping algorithms into the existing Cloud to Street optical flood detection pipeline in Google Earth Engine using the Python API

Provide SAR domain expertise on multidisciplinary teams (including machine learning engineers, hydrologists and others) to develop products using Sentinel-1 and passive microwave sensors and knowing when and where to task commercial radar sensors

Develop new algorithms to detect floods with commercial SAR sensors

Lead technical partnerships with commercial radar satellite companies

This role is based in our Brooklyn, NY office. Candidates based in New York City, Austin, TX, Santa Fe, NM, Boston MA, or Tucson, AZ areas are preferred. Remote work is possible within UTC -8 to UTC +1 time zones (Pacific Standard Time to Central European Time).

To Apply

Send resume hiring@cloudtostreet.info with “Radar Remote Sensing Scientist” in the subject line. Submissions should include an attached CV/resume and a paragraph of interest. Relevant past projects and/or publications are optional for submission.

More information: www.cloudtostreet.ai/radar-remote-sensing-scientist

 

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