Data Scientist 1 - Systems Modeling

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 Earth and Biological Sciences Directorate (EBSD) leads critical research in four areas: Atmospheric, Climate & Earth Sciences, Biological Sciences, Environmental Molecular Sciences, and Global Change. Our vision is to develop a predictive understanding of biological and Earth systems in transition. We aim to understand energy and material flows within the integrated Earth system; to understand, predict, and control the response of biosystems to environmental and/or genomic changes; and to Model the Earth system from the subsurface to the atmosphere.

The Environmental Molecular Sciences Division is comprised of 18 interdisciplinary research teams focused on deciphering molecular-level interactions driving biological and environmental processes across temporal and spatial scales. Through computational analysis and modeling, these findings contribute to predictive understanding of how systems respond to environmental perturbations thus enabling solutions to the nation’s energy, environmental, and human health challenges. The division also manages the Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus that accelerates the research of scientists around the world by providing access to world-class expertise, instrumentation, and computational resources.

The Computing, Analytics, and Modeling (CAM) group within the Environmental Molecular Sciences Division at PNNL is seeking a motivated Data Scientist 1 to contribute to next generation artificial intelligence solutions for mass spectrometry based omics and real time measurements. The ideal candidate will support the development of AI foundation models capable of interpreting raw mass spectrometry (MS) signals, collaborate on multi-agent scientific systems, and build machine-learning models optimized for real-time execution on microcontrollers and edge devices.

This role offers the opportunity to work with interdisciplinary teams advancing semi-autonomous science, HPC-scale training, and context-aware scientific AI systems and:

• Develop algorithms and computational tools for interpreting raw mass spectrometry data across proteomics, metabolomics, and related omics.
• Implement machine learning and deep learning models in Python and PyTorch, including model training, evaluation, and optimization.
• Contribute to the design, training, and deployment of AI foundation models for MS signal interpretation.
• Collaborate with team members to develop multi-agent systems that use foundation models for semi-autonomous, context-aware scientific workflows.
• Prototype and implement ML models for real-time inference on microcontrollers and other embedded or edge-computing platforms.
• Conduct performance optimization for embedded ML, including latency reduction and memory-efficient model architectures.
• Utilize high-performance computing (HPC) systems to train and scale deep learning models.
• Participate in code development, documentation, testing, and reproducibility practices across shared codebases.
• Collaborate with domain scientists, engineers, and software developers to integrate models into analytical pipelines and experimental workflows.
• Communicate technical findings, model performance, and design decisions clearly to team members and project stakeholders.
• Contribute to scientific writing, including the preparation of manuscripts and technical reports.

• BS/BA or higher

• Degree in computer science, Electrical and Computer Engineering, Bioinformatics, Statistics, Data Science, or a related field

• Experience developing algorithms or tools for raw mass spectrometry data (LC-MS, MS/MS)

• Proficiency in Python and experience developing AI/ML data analysis software using ML-related packages libraries (Pandas, Numpy, SciPy, SciKit) and PyTorch for building and training deep learning models

• Experience preparing data for model development, including signal processing or feature extraction for high-dimensional scientific data

• Familiarity with high-performance computing environments and distributed training workflows

• Experience implementing or optimizing ML models on edge hardware such as microcontrollers (e.g., ARM Cortex, ESP32, STM32) or single-board devices

• Understanding of embedded ML techniques such as model quantization, pruning, or conversion to formats suitable for edge deployment (e.g., TensorFlow Lite, ONNX)

• Experience with version control (e.g., Git) and collaborative software development practices

• Exposure to mass spectrometry-based omics workflows or scientific instrumentation data

• Interest in foundation models, multi-agent systems, or autonomous science frameworks

• Strong problem-solving skills and attention to detail

• Ability to work effectively in interdisciplinary team environments

• Clear written and verbal communication skills

This is not a Testing Designated Position (TDP).