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Geospatial Data EngineerOak Ridge National Laboratory • Oak Ridge, Tennessee, United States
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Geospatial Data Engineer

Oak Ridge National Laboratory
  • US
    Oak Ridge, Tennessee, United States
  • US
    Oak Ridge, Tennessee, United States
Jetzt Bewerben

Über

Requisition Id16118
Overview As a U.S. Department of Energy (DOE) Office of Science national laboratory, Oak Ridge National Laboratory (ORNL) has an extraordinary history of solving some of the nation’s most complex scientific and security challenges. ORNL’s mission is carried out by a dedicated and creative staff working across disciplines to accelerate scientific discovery and translate research into impactful energy, environmental, and national security solutions.
The Geospatial Data Modelling Group within the Human Dynamics Section, part of the Geospatial Science and Human Security Division at ORNL, is seeking a Geospatial Data Engineer to support research and operational workflows focused on scalable geospatial data science, applied machine learning, and production‑grade engineering practices to deliver repeatable, defensible, and time‑dynamic geospatial products in support of national security, humanitarian response, disaster assessment, and resilience planning.
In this technical role, the candidate will collaborate with an interdisciplinary team of human geographers, population scientists, geospatial analysts, data scientists, and software engineers. They will contribute across the full lifecycle of geospatial modeling efforts: data acquisition and preparation, feature engineering, model development and evaluation, MLOps and codebase maintenance, automation, and quality assurance. A key component of this position is building agentic AI workflows that help discover, gather, validate, and standardize open‑source data for downstream geospatial analytics and machine learning.
The position offers a unique opportunity to work on applied spatial analytics and geospatial data modeling at scale, leveraging diverse geospatial, demographic, and remotely sensed data sources. While the role does not require independent development of novel AI algorithms, it does require strong implementation skills, sound statistical judgment, and an ability to translate methods into reliable, maintainable, and well‑documented pipelines.
Major Duties and Responsibilities
Develop, maintain, and operationalize geospatial data science pipelines across ingestion, feature engineering, training, inference, evaluation, and delivery, using reproducible MLOps practices (version control, testing, experiment tracking, containerization, and CI/CD).
Support implementation of agentic AI workflows to discover, gather, and prepare data from open‑source repositories (e.g., catalogs, APIs, and bulk downloads), including provenance tracking, metadata extraction, and licensing/usage notes.
Build scalable geospatial data preparation and validation routines for raster and vector data (projection harmonization, spatial joins, tiling/chunking, and QA/QC).
Develop geospatial validation frameworks for model outputs (e.g., comparisons to reference datasets, spatial cross‑validation, summary dashboards, and automated report generation).
Support documentation, metadata development, and version tracking for data products and model releases; contribute to technical summaries, figures, and reports/publications as appropriate.
Participate in code reviews, model reviews, and data readiness reviews to ensure analytical defensibility, transparency, and fitness‑for‑use in operational and decision‑support contexts.
Collaborate with research staff to integrate new data sources, indicators, and modeling approaches into existing workflows; communicate clearly across technical and domain teams.
Basic Qualifications
Bachelor’s degree and 3+ year’s experience in Geography, GIScience, Computer Science, Data Science, Statistics, Engineering, or a related field with a strong quantitative and software development emphasis.
Demonstrated experience with geospatial analysis using Python in a production or research to production environment leveraging common geospatial libraries (e.g., geopandas, rasterio, shapely, pyproj) and/or enterprise GIS tooling (e.g., PostGIS).
Strong software engineering fundamentals: Git‑based workflows, testing, code review, and writing maintainable, well‑documented code.
Experience preparing and validating raster and vector datasets (data cleaning, transformation, projection/CRS management, and quality control).
Working knowledge of machine learning and statistical modeling concepts (e.g., regression, classification, clustering, model evaluation).
Ability to work effectively in a team‑based, production‑oriented research environment and communicate technical results to diverse stakeholders.
Preferred Qualifications
Master’s degree in a relevant discipline or equivalent applied experience in geospatial data science, MLOps, or applied machine learning.
Experience with modern MLOps tooling and practices (e.g., MLflow or equivalent experiment tracking, model registries, containerization, reproducible environments).
Experience building data pipelines and workflow orchestration (e.g., Airflow, Prefect, Dagster, Make/Snakemake) and working in Linux/HPC environments.
Experience with large, multi‑resolution geospatial datasets and performance‑oriented processing (tiling, chunking, parallelization; Dask/Spark a plus).
Experience using or building agentic/LLM‑enabled workflows for data discovery, extraction, and normalization, with attention to provenance, reproducibility, and quality.
Familiarity with uncertainty, data limitations, and bias in population and demographic modeling and in applied geospatial decision‑support contexts.
Active or eligible U.S. security clearance or ability to obtain one.
Special Requirements
Q clearance with SCI: This position requires the ability to obtain and maintain a Secret Compartmented Information (SCI) clearance from the Department of Energy. As such, this position is a Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing a pre‑placement drug test and participation in an ongoing random drug testing program. In addition, due the SCI, you may also be subject to random polygraph testing.
About ORNL As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.
ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well‑being of you and your family. Employee amenities such as on‑site fitness, banking, and cafeteria facilities are also available for added convenience.
Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.
If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT‑Battelle is an E‑Verify employer.
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  • Oak Ridge, Tennessee, United States

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