Science Systems and Applications, Inc. - Science and Technology with Passion

 

SSAI is an Equal Employment Opportunity and Affirmative Action Employer. EEO/AA-Minorities/Females/Veterans/Individuals with Disabilities

Scientific Programmer to Support Multispectral and Hyperspectral Remote Sensing Projects
Reference #:21-3136
Open Date:6/3/2021
Location:Greenbelt, MD
US Citizenship Required:No
  
Job Description:
Science Systems and Applications, Inc (SSAI) is seeking applications for a full-time Remote Sensing scientist/programmer (junior to mid-level) to join our productive team of scientists working on various NASA and USGS funded projects at NASA's Goddard Space Flight Center (GSFC). The current research focus is on algorithm development and validation for generating products in aquatic ecosystems from current and future national and international multispectral and hyperspectral missions. The selected candidate will have a strong background in one at least of the following areas:

1. Aquatic/terrestrial/atmospheric remote sensing
2. Data/image analysis
3. Image processing
4. Optics
5. Ocean/aquatic sciences/biology
6. Hydrological modeling.

Demonstrated skill in code development for data analysis and processing pipeline automation is required. The selected candidate is expected to have excellent written and verbal communication skills and be willing to contribute to peer-reviewed publications.

A remote work option is available until GSFC opens fully but, at that time, the selected candidate will work either onsite at GSFC or in SSAI's nearby HQ facility.
Required Qualifications:
M.S. degree in one of the following (Physics/Engineering/Oceanography/Hydrology/Geography) + 2-3 years experience, or a PhD with minimal experience.

Programming: Python, R, or C.

Platforms: Linux, High Performance Computing.

U.S. citizenship is not required, but successful candidate must be able to quickly qualify for NASA badging and IT access.


Desired Qualifications:
Experience in the use of NASA remote sensing data is helpful, but not required.

Familiarity with machine learning and radiative transfer concepts is desirable.

Previous experience at GSFC is helpful, but not required.