Job description

Purpose of the Position: The selected candidate will support the Office of Data Science and Informatics (ODSI) within the Earth Systems Science Center. This role focuses on advancing data science capabilities to support NASA Earth science data management, access, and dissemination. This role is suited for candidates with a strong research and computational background who can operate effectively across disciplinary boundaries and contribute to both scientific discovery and operational system development within NASA Earth science programs. ODSI evaluates trends across data science and informatics communities to inform strategic direction and develop effective, scalable solutions. The office also provides a user-centered perspective on how Earth science data are represented, communicated, and utilized. The position contributes to cross-disciplinary collaboration aimed at improving all phases of the Earth science data lifecycle, including policy, engineering, workflows, and information delivery. Duties / Responsibilities: Prepare and curate validation datasets to support the development and assessment of AI and machine learning models Evaluate model performance using appropriateConduct integration testing to ensure models operate effectively within research and operational systems statistical and computational methodologies Identify and define new use cases for AI applications across Earth science domains Develop and implement software solutions across multiple projects in both research and operational contexts Minimum Requirements: • Master’s degree in Atmospheric Science, Earth Science, Remote Sensing, or a related field; or an equivalent combination of education and relevant experience. • 2.5–3 years of full-time, verifiable professional experience. • Extensive experience working with geospatial datasets and associated technologies. • Hands-on experience with numerical modeling for Earth science applications. • Demonstrated record of peer-reviewed publications. • Strong programming background (e.g., Python, C/C++, or similar scientific computing languages). • Excellent written and verbal communication skills. • Demonstrated ability to reason through complex, interdisciplinary problems, including those outside one’s primary area of expertise. Desired Qualifications: • Ph.D. in Atmospheric Science, Earth Science, Remote Sensing, or a related field; or equivalent experience. • Experience with proposal development and grant writing. • Familiarity with AI/ML model development, evaluation, and deployment in scientific or operational environments. Published Salary (if available): $88,528 - $94,170

Requirements

Must-haves

Prepare and curate validation datasets to support the development and assessment of AI and machine learning models
Evaluate model performance using appropriateConduct integration testing to ensure models operate effectively within research and operational systems statistical and computational methodologies
Identify and define new use cases for AI applications across Earth science domains
Develop and implement software solutions across multiple projects in both research and operational contexts
Master’s degree in Atmospheric Science, Earth Science, Remote Sensing, or a related field; or an equivalent combination of education and relevant experience.
2.5–3 years of full-time, verifiable professional experience.
Extensive experience working with geospatial datasets and associated technologies.
Hands-on experience with numerical modeling for Earth science applications.
Demonstrated record of peer-reviewed publications.
Strong programming background (e.g., Python, C/C++, or similar scientific computing languages).
Excellent written and verbal communication skills.
Demonstrated ability to reason through complex, interdisciplinary problems, including those outside one’s primary area of expertise.
$88,528 - $94,170

Nice-to-haves

Ph.D. in Atmospheric Science, Earth Science, Remote Sensing, or a related field; or equivalent experience.
Experience with proposal development and grant writing.
Familiarity with AI/ML model development, evaluation, and deployment in scientific or operational environments.

Application prep

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Keywords to mirror

aimachine learningpythonml

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