We are seeking a motivated Associate Data Scientist for our Geospatial AI team. The role will support research and operational applications of AI for water resources monitoring, and potentially other use cases for remote sensing data of Earth including critical mineral identification, hydrologic monitoring, and environmental change detection. This work requires expertise in remote sensing, data analytics, and earth system processes to derive actionable insights from satellite and airborne observations, while advancing remote sensing capabilities to support water security and U.S. energy independence. Multi-level experience in multispectral and hyperspectral data, synthetic aperture radar data, as well as lidar data is needed to integrate remote sensing products with hydrologic and land surface models. This will improve prediction and assessment of water quality and availability, and support large-scale geospatial data analyses using cloud-based platforms. The role will involve collaboration with the U.S. Geological Survey (USGS), and may also involve support for the development of proposals for new collaborations involving other government organizations,, academia and industry, as part of joint public-private initiatives aimed at addressing key national challenges.

Essential Duties/Responsibilities:

Satisfy USRA's non-profit mission by making a distinctive impact through scientific and technical accomplishments in the assigned projects.

Train, test and evaluate geospatial machine learning models that utilize satellite data for water resource use cases, using distributing computing approaches, including models for the following:

estimation of optically active water quality constituents such as chlorophyll, turbidity, and dissolved organic matter, from Sentinel-2 aquatic reflectance data.

detection of historical algal blooms at a global scale, including phycocyanin data retrieval from Sentinel-2 instrumentation.

Co-author research papers for leading scientific journals and present findings at relevant scientific conferences.

Engage academic, industrial, and government research communities in collaborative research through activities such as seminars and other talks, workshops, and conferences.

Act as a mentor for student interns involved in machine learning research as needed.

Maintain an awareness of funding opportunities, and support and/or lead proposal activities as approved.

Act safely, ethically and in compliance with all laws and regulations and USRA policies and procedures including:

Ensure the protection of intellectual property including submitting new technology disclosures and supporting patent applications.

Ensure export control compliance related to ITAR/EAR regulations.

Ensure compliance with all policies such as for timesheets and travel.

Complete all compliance trainings as scheduled.

Additional Responsibilities

Model USRA's core values: Discovery, Innovation and Service

Complete ongoing learning and development staying abreast of the state-of-the-art

Other job duties as assigned.

Required Qualifications

Education:

A Master's Degree or higher in Geological/Geophysical Engineering, Remote Sensing, Geography, or a related discipline is required. PhD strongly preferred.

Experience:

Minimum 2 years hands-on experience developing and testing data driven biophysical and geophysical models using satellite datasets such as Landsat, MODIS, and Sentinel-1/2/3, including training, testing and evaluating machine learning models with satellite data applied to water resources.

Experience co-authoring papers and presentations for academic audiences with a record of peer reviewed publications or contributions to operational remotely sensed hydrologic data products.

Familiarity with federal agency remote sensing data standards and workflows, knowledge of data assimilation or model coupling techniques, and experience using cloud-based aquatic reflectance datasets is a plus.

Knowledge, Skills and Abilities:

Strong programming proficiency in Python and extensive experience with core data science tools including PyTorch Lightning, NumPy, XArray, Pandas, Scikit-learn, Lightning, Kubernetes & Git.

Solid theoretical understanding of fundamental machine learning and deep learning concepts (e.g., CNNs, RNNs, transformers).

Proficiency in geospatial programming and analysis in Python and R; and expert skills with GIS software including ArcGIS and QGIS.

Strong understanding of hydrologic processes and their representation in remote sensing and modeling frameworks is essential,

Excellent problem-solving skills and the ability to work with complex geospatial datasets.

Strong verbal and written communication skills.

Ability to prepare scientific publications as co-author with other scientists

Excellent written and verbal communication in English is required.

Working Conditions:

This is a remote work position.

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