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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Scientist IV - Machine Learning for GEOINT and Remote Sensing - **Company:** Pacific Northwest National Laboratory - **Location:** Richland, WA, United States - **Experience:** Expert - **Salary:** $166,800 - **Contract:** Temporary contract - **Skills:** Training Data, Artificial Intelligence, Geospatial Intelligence, Intelligence Analysis, Python (Programming Language), Machine Learning, Tensorflow, Data Processing, Pytorch, Transfer Learning, Deep Learning, Agentic-AI, Few-Shot Learning, Low Latency, Data Analytics - **Published:** November 8, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3505242239&tx=FK1916FFG&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * BS/BA and 7+ years of data science experience -OR- * MS/MA and 5+ years of data science experience -OR- * PhD with 3+ years of data science experience * Deep learning expertise, including developing, adapting, and evaluating algorithms or analytical methods for detection and classification in electro-optical (EO) imagery or target detection and material classification in hyperspectral imagery (HSI). * Experience developing reproducible research code in Python with PyTorch or TensorFlow., * 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 methods such as few-shot learning, transfer learning, or open-vocabulary models. * Experience with multimodal or temporal fusion for geospatial analysis, or agentic workflows that coordinate 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, or developing and 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, developing research proposals, or securing funding or other support for research. Hazardous Working Conditions/Environment Not applicable. Additional Information This position requires the ability to obtain and maintain a federal security clearance. A security clearance background investigation includes review of your employment, education, financial, and criminal history, as well as interviews with you and your personal references, neighbors, and co-workers to determine trustworthiness, reliability, and loyalty to the United States. The investigation also examines your foreign connections, drug and alcohol use, foreign influence, and overall conduct., * U.S. Citizenship * Background Investigation: Applicants selected will be subject to a Federal background investigation and must meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B. * Drug Testing: All Security Clearance positions are Testing Designated Positions, which means that the applicant selected for hire is subject to pre-employment drug testing, and post-employment random drug testing. In addition, applicants must be able to demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the requisite Questionnaire for National Security Positions (QNSP). Note: Applicants will be considered ineligible for security clearance processing by the U.S. Department of Energy if non-use of illegal drugs, including marijuana, for 12 months cannot be demonstrated. ## Description 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. In the AI and Data Analytics Division, you will shape research directions, develop algorithms and analytical methods, lead interdisciplinary project teams, and mentor researchers. You will work with 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 role also offers opportunities to explore related problems in remote sensing and intelligence analysis. Candidates with deep learning experience in EO, HSI, or related sensing applications are encouraged to apply. You will work alongside researchers and domain specialists on difficult national science and technology challenges. Collaboration with software and machine learning (ML) engineers will reflect each project's needs and stage of development. Opportunities to advance existing research and pursue new ideas may come through ongoing projects, internal research investments, or new proposals., * Define research questions and develop or adapt methods to address them. Investigate model behavior and solve difficult technical problems alongside 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 ML 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 which research directions to pursue. * 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.