Research Scientist II, Step 1-4

Duties / Responsibilities

• Execute applied research and co-develop Earth observation-driven solutions focused on agricultural applications, water resources, irrigation, and hydrology for state, local, tribal, territorial, and private sector stakeholders.
• Develop, calibrate, and apply advanced land surface hydrologic, hydraulic, and agricultural models—coupled with crop-specific remote sensing techniques—to address issues such as flood mapping, drought monitoring, cropland/rangeland monitoring, and precision agriculture.
• Support applied research and application development for reservoir management and the monitoring of surface water levels to inform regional water resource planning.
• Apply geospatial artificial intelligence and machine learning workflows to extract actionable insights from multi-source remote sensing datasets, including extensive use of very high-resolution commercial satellite imagery.
• Develop and apply Earth observation workflows for crop monitoring and agricultural resilience applications.
• Provide technical and scientific expertise in areas related to consumptive water use, irrigation demand, water rights management, and drought resilience, actively leveraging tools and datasets such as OpenET.
• Translate complex Earth science data and model results into actionable, "last-mile" intelligence and decision-support tools for end-users.
• Lead publications, data products, and presentations at scientific and stakeholder-focused conferences.
• Provide coordination support and capacity-building/knowledge-transfer activities with domestic partner organizations and end-users.

Requirements

• Master’s degree in Hydrology, Agricultural, Engineering, Earth Science, Geospatial Science, Geography, Environmental Science, or a related field; (Bachelor's and experience in a specialized area may be substituted for a degree).
• Minimum of 1 year verifiable full-time work experience in these disciplines.
• Exceptional understanding of the intersection of hydrology and agriculture, including demonstrable experience with water resource and/or crop-specific remote sensing and modeling, consumptive water use, and irrigation management.
• Deep expertise in geospatial artificial intelligence; should have a proven track record of applying machine learning and AI techniques specifically to spatial and Earth observation data.
• Experience processing, analyzing, and deriving products from very high-resolution satellite imagery.
• Demonstrated proficiency with relevant modeling and monitoring applications, including land surface hydrologic modeling, hydraulic routing, surface water level assessments, and reservoir management workflows.
• Experience developing decision-support systems for agricultural monitoring, irrigation prioritization, crop growth cycle assessment, and yield modeling.
• Working knowledge of practical domestic water resource frameworks, including water rights and the application of tools like OpenET for evapotranspiration and consumptive use monitoring.
• Skilled in the use of Python, R, or other programming languages specialized for spatial data science, raster data processing automation, and machine learning pipelines.
• Excellent verbal and written communication skills to effectively collaborate with interdisciplinary science teams and translate technical concepts to non-technical domestic stakeholders.