Lead Data Scientist IV - 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 Lead Data Scientist IV 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). This research supports geospatial intelligence (GEOINT) and national missions. The role is part of the AI and Data Analytics Division within the National Security Directorate, which develops computational solutions to complex data and analytic challenges across the data analytics lifecycle. The role shapes research directions, develops algorithms and analytical methods, leads interdisciplinary project teams, and mentors researchers. The successful candidate will collaborate with software and machine learning engineers to prepare methods for operational use. 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 position may also explore related problems in remote sensing and intelligence analysis. Research opportunities may arise through ongoing projects, internal research investments, or new proposals. The position is based in Richland, Washington, or Seattle, Washington, and requires an onsite presence. The candidate must be able to obtain and maintain a federal security clearance and meet applicable U.S. citizenship and background-investigation requirements. The position is a Testing Designated Position subject to pre-employment and random drug testing.

Requirements

Bachelor's degree and 7 or more years of data science experience, or master's degree and 5 or more years of data science experience, or Ph.D. and 3 or more years of data science experience.
Deep learning expertise, including developing, adapting, and evaluating algorithms or analytical methods for detection and classification in EO imagery or target detection and material classification in HSI.
Experience developing reproducible research code in Python with PyTorch or TensorFlow.
Ability to obtain and maintain a federal security clearance.
U.S. citizenship.
Must meet eligibility requirements for access to classified matter, including a federal background investigation.
Must meet the stated drug-testing requirements, including demonstrating non-use of illegal drugs for the 12 consecutive months preceding completion of the required Questionnaire for National Security Positions.

Nice To Haves

Experience defining research directions for complex problems and leading interdisciplinary project teams.
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 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, including collaboration with software or ML engineering teams.
Experience developing and evaluating models for edge or onboard platforms with size, weight, and power constraints, balancing accuracy, latency, memory, and energy use.
Experience supporting national security missions, developing research proposals, or securing funding or other support for research.

Responsibilities

Define research questions and develop or adapt methods to address them; investigate model behavior and solve difficult technical problems with the project team.
Guide interdisciplinary project teams through complex research, adapting methods and plans as findings emerge or mission needs change.
Work with mission partners to establish evaluation criteria and assess detection performance, false alarms, robustness, and computational demands under realistic conditions.
Work with software and machine learning engineers to prepare methods for operational use, addressing research limitations that affect performance or reliability.
Share methods and findings with collaborators and mission partners to inform decisions about research directions.
Shape new research ideas and collaborate on proposals that create opportunities for the team to pursue them.
Mentor researchers through technical discussions, feedback, and opportunities to take on more challenging work.
Help recruit colleagues and connect people with complementary expertise across teams and partner organizations.
Guide project teams in following applicable quality, safety, and security requirements.