Geospatial Data Engineer

Oak Ridge National Laboratory seeks a Geospatial Data Engineer to support scalable geospatial data science, applied machine learning, and production-grade engineering practices delivering geospatial products for national security, humanitarian response, disaster assessment, and resilience planning. The role encompasses the complete geospatial modeling lifecycle: data acquisition and preparation, feature engineering, model development and evaluation, MLOps, automation, and quality assurance. A central focus involves building agentic AI workflows that help discover, gather, validate, and standardize open-source data for downstream analytics and machine learning applications. Major duties include developing and operationalizing geospatial data science pipelines using reproducible MLOps practices (version control, testing, experiment tracking, containerization, CI/CD), supporting agentic AI workflows for data preparation with provenance tracking, building scalable data preparation and validation routines for raster and vector data, and developing geospatial validation frameworks for model outputs. Requires Bachelor's degree and 3+ years experience in Geography, GIScience, Computer Science, Data Science, Statistics, or Engineering with demonstrated production geospatial analysis experience using Python (geopandas, rasterio, shapely, pyproj) and/or enterprise GIS tooling (PostGIS). Preferred: Master's degree, MLOps tooling experience, workflow orchestration (Airflow, Prefect, Dagster). Requires ability to obtain SCI security clearance.