Data Scientist II - Machine Learning for GEOINT and Remote Sensing

Pacific Northwest National Laboratory•Richland, WA

1d•Onsite

About The Position

The Data Scientist II will apply deep learning to develop methods for detecting and classifying targets in electro-optical (EO) imagery or identifying targets and materials in hyperspectral imagery (HSI). This research supports geospatial intelligence (GEOINT) and national missions within the AI and Data Analytics Division of PNNL’s National Security Directorate. The division develops computational solutions to complex data and analytic challenges, integrating research, engineering, and operations across the data analytics lifecycle. The position independently carries out defined research tasks as part of a team developing methods and tools for intelligence analysis and national missions. 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. The role may also address related problems in remote sensing and intelligence analysis. The scientist will collaborate with researchers, domain specialists, software engineers, and machine learning engineers according to project needs and development stages. Opportunities include advancing ongoing research and pursuing new ideas through existing 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 selected applicant is subject to federal background investigation and pre-employment and random drug testing.

Requirements

Bachelor’s degree and 2 years of data science experience, or a master’s degree, or a Ph.D.
Experience applying deep learning to EO detection and classification or HSI target detection and material classification.
Experience developing deep learning methods using PyTorch or another deep learning framework.
Ability to obtain and maintain a federal security clearance.
U.S. citizenship.
Selected applicants must meet eligibility requirements for access to classified matter and complete the required federal background investigation.
For this Testing Designated Position, the selected applicant must undergo pre-employment and random drug testing and meet the stated federal drug-use eligibility requirements.

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 using 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 developing or evaluating models for edge or onboard platforms with size, weight, and power (SWaP) constraints, balancing accuracy, latency, memory, and energy use.
Experience supporting national security missions.

Responsibilities

Apply and adapt deep learning methods to detection and classification problems.
Investigate research questions through algorithm development, mathematical analysis, experiments, or implementation.
Design reproducible experiments and compare methods, examining detection performance, classification errors, false alarms, and limitations.
Document methods and results, and work with software and machine learning engineers to integrate tested research code and algorithms into usable tools.
Plan and carry out research tasks, discuss progress and challenges with project leads, and contribute ideas to research plans and proposals.
Share technical knowledge across the team and mentor junior colleagues or students as appropriate.
Follow applicable quality, safety, and security requirements.