| Purpose of the Position: |
The EarthRISE Project Office, within the Lab for Applied Science (LAS) under the Earth System Science Center (ESSC), is seeking a highly motivated Data Science Research Scientist with applied experience in computer science, geospatial artificial intelligence (Geo-AI), machine learning, cloud computing, and remote sensing/geospatial datasets to join our interdisciplinary science team. The Data Science Research Scientist will play a key role in the team by co-developing Earth-observations-driven data solutions and geospatial artificial intelligence-informed services and supporting workforce development activities in support of the EarthRISE program. The Data Science Research Scientist will also be responsible for supporting an array of cross-cutting thematic needs in expanding the applied research portfolio of EarthRISE’s Data Science efforts as well as LLM and Geo-AI Integration. A successful candidate will have a strong background in the fields of computer science, remote sensing, agentic workflows, and machine learning algorithms. As part of our diverse, highly dynamic group, you will work side-by-side with a team of exceptional scientists to translate Data Science into actionable insights for decision-makers and stakeholders, while fostering your career growth and development.
EarthRISE is a project of the National Aeronautics and Space Administration (NASA) that works in collaboration with state, local, tribal, territorial, and private sector partners to use information provided by Earth observing satellites and geospatial technologies for managing environmental challenges. EarthRISE’s goal is to empower decision-makers with tools, products, geospatial artificial intelligence, and services to act locally on issues across many thematic areas including disasters, agriculture, water, ecosystems, air quality, and health.
The EarthRISE Project Office in Huntsville, AL, offers a flexible and dynamic work environment, with an in-house team of over 30 science professionals and numerous opportunities for growth and professional development. With a growing economy, diverse population, low cost of living, and vibrant city life, Huntsville is consistently ranked among the top places to live in the U.S.
Duties / Responsibilities:
A Data Science Research Scientist plays a key role in co-developing and evaluating Geo-AI LLM-integrated geospatial workflows and/or robust, predictive models and delivering cutting-edge scientific breakthroughs to decision makers.
● Provide Data Science and technical expertise across a set of thematic areas in support of EarthRISE.
● Work together with the Data Science team as well as the program Chief Scientist and activity managers to provide strategic guidance to EarthRISE office on integration of emerging data science best practices in end-user solutions.
● Support EarthRISE office throughout the project lifecycle (planning, execution, and follow-up) to catalyze the use of advanced data science techniques in support of end-user solutions.
● Support or lead open science work via publications, special journal issues, data products/services, and presenting at scientific conferences.
● Contribute to or lead scientific publications.
● Coordinate and collaborate with broader EarthRISE teams at MSFC and other NASA centers.
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| Minimum Requirements: |
• Master’s degree in Computer Science, Engineering, Earth Science, Geosciences, Environmental Science, or other highly quantitative discipline (Bachelor's degree and experience in a specialized area may be substituted for a degree).
• Minimum of 1 year of verifiable, full-time work experience in these disciplines.
• Passion for applied research and translating science into action on the ground
• Experience developing, configuring, and evaluating Large Language Models (LLMs), including open-source and commercial models (e.g., Llama, Mixtral, Gemma, GPT-class models) for scientific reasoning and Earth Observation applications.
• Skilled in the use and development with Python, R, JavaScript, C, SQL, and other programming languages, frameworks, and libraries for research, data analysis, and/or modeling
• Skilled in designing and deploying agentic AI workflows using frameworks such as LangGraph, CrewAI, AutoGen, Model Context Protocol (MCP), and Agent-to-Agent (A2A) architectures that integrate geospatial tools, APIs, and scientific data sources.
• Demonstrated experience building Retrieval-Augmented Generation (RAG) systems using vector databases, embedding models, knowledge graphs, and Earth science repositories to improve factual accuracy and scientific traceability.
• Experience developing AI evaluation and benchmarking frameworks for assessing hallucinations, reasoning quality, factual consistency, uncertainty, robustness, reproducibility, and task performance using tools such as LangSmith, DeepEval, Ragas, MLflow, or equivalent evaluation platforms.
• Knowledge of Responsible AI, Trustworthy AI, and AI governance principles, including explainability, transparency, provenance, reproducibility, model documentation, risk assessment, and human-in-the-loop validation for scientific decision support.
Skilled in LLMOps and MLOps practices including model deployment, versioning, experiment tracking, inference optimization, containerization (Docker), Kubernetes, CI/CD pipelines, monitoring, and scalable AI infrastructure as well as Git.
• Experience designing provenance-aware scientific workflows that capture data lineage, model lineage, workflow metadata, citation tracking, and reproducible computational pipelines supporting Open Science principles.
• Skilled as a strong science communicator.
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| Desired Qualifications: |
• 4 or more years of experience working in these disciplines is desired.
• Experience working with NASA and/or other federal, state, local, tribal, or private sector organizations is desired.
• Earth science geospatial data analysis experience, including math, statistical & scientific data processing automation, and raster data processing of temporal datasets is desired.
• Experience integrating AI systems with cloud-native geospatial platforms including STAC APIs, Cloud Optimized GeoTIFFs (COGs), Zarr, Earthdata services, Google Earth Engine, AWS Open Data, and distributed geospatial processing environments is desired.
• Experience in writing and developing technical manuscripts is desired.
• Record of publication commensurate with level of experience is desired.
• Experience integrating multimodal AI and foundation models including Vision Transformers (ViTs), Earth Observation Foundation Models, multimodal LLMs, and geospatial deep learning architectures for image, raster, and text analysis is desired.
• Demonstrated ability to develop and validate autonomous scientific decision-support systems by combining Earth Observation datasets, statistical methods, machine learning, and LLM-based reasoning to support operational and policy-relevant applications is desired.
• Experience in developing, tuning, and deploying machine learning-based algorithms, including geospatial artificial intelligence to address real-world problems is desired.
• Strong experience teaching, skills building, curriculum development, knowledge management, and workforce development is desired.
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