Support Scientist II

STC • Greenbelt, MD

3d • $90,000 - $105,000 • Hybrid

About The Position

We are seeking a Support Scientist II to join a team supporting cloud-based workflows for NASA Earth science data at the Goddard Space Flight Center (GSFC) on the ATMOS contract. This role focuses on integrating scientific expertise with software development to enable next-generation analysis of remote sensing and modeling datasets. The position will support emerging work in vegetation remote sensing, ecological modeling, and multi-instrument data fusion , with an emphasis on developing scalable, cloud-native workflows and data products.

ATMOS is a NASA support services contract that provides advanced scientific, engineering, and technical expertise to enable Earth and space science missions. The program supports research, data analysis, technology development, and mission operations across multiple NASA centers. Through ATMOS, our teams deliver innovative solutions that help advance NASA’s mission and expand scientific discovery.

This is an opportunity to work at the intersection of Earth system science, data science, and cloud computing , supporting NASA missions and research initiatives.

Requirements

• 2+ years of research experience in atmospheric or related sciences.
• Experience working with scientific datasets and data analysis workflows
• Strong analytical and problem-solving skills
• Ability to work effectively in a collaborative, interdisciplinary environment
• Strong written and verbal communication skills
• Strong background in programming (FORTRAN, Python, C/C++).
• Familiarity with visualization/analysis tools such as IDL, MATLAB, or LabVIEW.
• Ability to meet NASA IT security requirements and obtain/maintain a NASA badge
• Requires U.S. Citizenship
• Ph.D. in atmospheric science, physical science, mathematics, computer science, or equivalent.

Responsibilities

• Develop new science data products from remote sensing observations using a combination of physically based models (e.g., radiative transfer) and data-driven approaches (e.g., machine learning).
• Support model–data fusion efforts across terrestrial ecology, atmospheric science, and related Earth system domains.
• Apply data assimilation, downscaling, and predictive modeling techniques to generate advanced Earth observation datasets.
• Design and execute targeted model simulations related to ecological processes, vegetation dynamics, and natural resource applications.
• Develop and apply machine learning and hybrid modeling workflows for feature extraction, classification, and regression analysis of Earth science datasets.
• Evaluate and validate AI/ML models for environmental data applications.
• Support multi-instrument and multi-scale data fusion, including harmonization of datasets from diverse remote sensing platforms.
• Analyze and process airborne and satellite datasets, including lidar, hyperspectral, and high-resolution imagery.
• Contribute to development of computational approaches for characterizing geospatial phenomena such as vegetation structure, fuel loads, and fire risk.
• Develop and maintain cloud-native workflows for data processing, analysis, and modeling in environments such as Amazon Web Services (AWS) and Microsoft Azure.
• Support transformation of datasets into analysis-ready, cloud-optimized formats.
• Collaborate on scalable data pipelines and computational workflows for large geospatial datasets.
• Work closely with NASA scientists to translate research needs into computational solutions.
• Contribute to scientific publications, technical documentation, and conference presentations.
• Participate in collaborative research efforts supporting NASA Earth science missions and programs.

Benefits

• Paid Time Off Starting at 80 hrs/yr, 11 Federal holidays, and 40 hrs/yr Sick Leave
• 401K with up to 4% employer matching contribution
• Comprehensive Medical, Dental, Vision Insurance, Short Term/Long Term Disability
• Flexible spending account
• Health savings account
• Tuition reimbursement

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree