Senior Data Scientist III - GEOINT/Remote Sensing

Pacific Northwest National Laboratory•Richland, WA

1d•Onsite

About The Position

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus. The National Security Directorate (NSD) drives science-based, mission-focused solutions to take on complex, real-world threats to our nation and the world. The AI and Data Analytics Division, part of NSD, combines profound domain expertise and creative integration of advanced hardware and software to deliver computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing the tools to innovate quickly and field results faster. Our strengths are integrated across the data analytics lifecycle, from data acquisition and management to analysis and decision support.

Requirements

BS/BA and 5+ years of relevant work experience -OR- MS/MA and 3+ years of relevant work experience -OR- PhD with 1+ year of relevant experience
U.S. Citizenship
Ability to obtain and maintain a federal security clearance.
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).
Must pass a drug test prior to commencing employment.
Must successfully complete the applicable tier of federal background investigation post hire and receive a favorable federal adjudication.
Must disclose any affiliation with the government of a country DOE has identified as a “country of risk” and either recuse yourself or receive approval from DOE and Battelle prior to employment.

Nice To Haves

Degree in data science, computer science, physics, mathematics, or similar discipline.
Expertise in GEOINT workflows, including experience with remote sensing platforms, multi-modal data fusion, and advanced geospatial analytics.
Experience working with hyperspectral imaging data in the LWIR, including: Developing and validating machine learning–based detection, classification, and unmixing algorithms, Full product pipeline experience (radiometric calibration, atmospheric correction, geometric correction, etc.), Strong spectral sensing fundamentals (radiative transfer, reflectance/emissivity, absorption features, SNR/noise modeling, sensor characterization)
Proficiency with hyperspectral analysis and visualization tools (e.g., ENVI/IDL)
Experience working with Synthetic Aperture Radar (SAR) data (complex/IQ and formed products), including: Developing and validating both traditional signal-processing and machine learning–based detection and classification algorithms, End-to-end SAR image product conversion pipeline experience
Proficiency with SAR rendering tools
Strong knowledge of SAR/RADAR theory fundamentals (wave propagation, scattering, speckle, Doppler/geometry, calibration, etc.)
Hands-on experience with geospatial analysis tools (e.g., GDAL, GeoPandas, Google Earth Engine) and machine learning frameworks (e.g., TensorFlow, PyTorch).
Experience training, fine-tuning, and applying deep learning models for remote sensing and GEOINT applications, including Automatic Target Recognition (ATR), object detection, and segmentation, using models and frameworks such as YOLO, Detectron2, DINOv2, Segment Anything Model (SAM), Vision Transformers, and geospatial foundation models.
Proven proficiency in creating proposals and technical reports.
Strong communication and collaboration skills, with the ability to present technical findings to diverse audiences and work effectively within multi-disciplinary teams.

Responsibilities

Drives the execution of research by developing, testing, and deploying ML models and geospatial analytics workflows.
Takes ownership of defined tasks or small projects, proactively identifying technical challenges and proposing solutions to senior staff or project leads.
Engages with stakeholders to understand project requirements and translate them into actionable technical approaches aligned with sponsor goals.
Serves as a mentor to junior staff or interns within the team, fostering a collaborative and inclusive research environment.
Supports proposal development and business development my contributing to proposals and/or briefing sponsors.
Builds effective working relationships within your immediate team, as well as across interdisciplinary teams at the group or division level.