Senior Data Scientist III - Machine Learning for GEOINT and Remote Sensing

Pacific Northwest National Laboratory•Richland, WA

2d•Onsite

About The Position

Pacific Northwest National Laboratory (PNNL) is seeking a Senior Data Scientist III with deep learning expertise to develop methods for detecting and classifying targets in electro-optical (EO) imagery or identifying targets and materials in hyperspectral imagery (HSI). The research supports geospatial intelligence (GEOINT) and national missions. The position is within the AI and Data Analytics Division of the National Security Directorate, which develops computational solutions for complex data and analytic challenges across the data analytics lifecycle. The role leads research projects or major technical efforts, taking methods from initial ideas through evaluation and integration into research prototypes. Current priorities include automatic target recognition (ATR), long-wave infrared (LWIR) HSI target detection and material classification, and detection and classification with limited labeled training examples, particularly in EO imagery. There may also be opportunities to pursue related problems in remote sensing and intelligence analysis. The scientist works with researchers, domain specialists, software engineers, and machine learning engineers, according to project needs, and may advance existing research or develop new ideas through ongoing projects, internal research investments, or proposals. The position is based in Richland, Washington, or Seattle, Washington, and requires an onsite presence. It requires the ability to obtain and maintain a federal security clearance and U.S. citizenship. The position is a Testing Designated Position and entails pre-employment and random drug testing. Candidates must meet federal background investigation and eligibility requirements for access to classified matter.

Requirements

One of the following education and experience combinations: BS/BA and 5+ years of data science experience; MS/MA and 3+ years of data science experience; or PhD and 1+ year of data science experience.
Experience applying and evaluating deep learning methods for detection and classification in electro-optical imagery, or target detection and material classification in hyperspectral imagery.
Experience developing deep learning methods using PyTorch or another deep learning framework.
Ability to obtain and maintain a federal security clearance and meet eligibility requirements for access to classified matter.
U.S. citizenship.
For security clearance processing, demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the Questionnaire for National Security Positions.

Nice To Haves

Experience with the EO or HSI imaging chain, including sensor response, image formation, calibration, and data processing; LWIR HSI experience is particularly valued.
Experience addressing limited labeled training data or new target categories through methods such as few-shot learning, transfer learning, or open-vocabulary models.
Experience with multimodal or temporal fusion for geospatial analysis, or agentic workflows coordinating models, geospatial tools, and data sources, including evaluation of workflow reliability and analytic results.
Experience translating research into methods or tools used in applied settings, or developing and evaluating models for edge or onboard platforms with size, weight, and power constraints while balancing accuracy, latency, memory, and energy use.
Experience supporting national security missions or developing proposals.

Responsibilities

Develop and adapt detection and classification methods through algorithm development, mathematical analysis, experimentation, or implementation.
Lead research projects or major technical efforts; coordinate contributors; and plan work, resources, and milestones.
Evaluate methods against appropriate baselines using representative data, investigating classification errors, false alarms, robustness, and limitations affecting use.
Build and validate research prototypes with software and machine learning engineers, addressing technical barriers to integration and deployment.
Work with collaborators to translate mission or user needs into research questions, and communicate findings through analyses, technical reports, and discussions.
Develop research ideas and collaborate with project leaders on proposals to support research.
Mentor colleagues, help recruit and onboard researchers, and share methods and findings across disciplines.
Follow applicable quality, safety, and security requirements.