Data Scientist – Computer Analytics & Modeling

Pacific Northwest National Laboratory is hiring a Data Scientist 2 for its Environmental Molecular Sciences Division in Honolulu, HI. The role involves advancing computational science, data analytics, and modeling to support research in biological and environmental processes. The candidate will collaborate with interdisciplinary teams and contribute to AI solutions within the division's strategic science objectives, focusing on understanding energy and material flows in Earth systems.
Position: Data Scientist 2 – Computer Analytics & Modeling

Overview

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.
Responsibilities

The Environmental Molecular Sciences Division’s (EMSD’s) Computing, Analytics, and Modeling (CAM) group focuses on advancing the science of the Environmental Molecular Sciences Laboratory (EMSL) user facility and the mission science of its sponsor, the DOE Office of Science’s Office of Biological and Environmental Research (BER) mission, by delivering world-class capabilities and developments in computational science, data analytics and transformations, and modeling sciences.

The group, which reports to the CAM Group Leader, works with researchers and staff in EMSL’s other two science areas (Environmental Transformations and Interactions, or ETI, and Functional and Systems Biology, FSB) to deliver on EMSL’s three strategic science objectives:
Digi Phen (Digital Phenome), MONet (Molecular Observation Network), and MIDAS (Modeling, Integration, and Data Agents for Science). Because data and computing infrastructure systems are critical to the group’s work, CAM also works closely with the group led by EMSL’s Chief Data Officer.

The Computing, Analytics, and Modeling (CAM) Group within the Environmental Molecular Sciences Division at PNNL is seeking a motivated Data Scientist 2 to contribute to cutting edge AI solution for computational and modeling research across the BER mission space. The role requires experience in designing and implementing AI-based agents and agentic workflows, along with a solid understanding of key tools such as Lang Chain, Lang Graph, and Model Context Protocol (MCP).

Candidates should have a proven ability to leverage AI to accelerate the software lifecycle and improve data exploration and retrieval, as well as experience supporting various stages of the data lifecycle, including data modeling, harmonizing data models, managing distributed or federated data, and organizational data governance. Additional expertise in metabolic modeling techniques such as flux balance analysis and metabolic control analysis, and familiarity with structural biology data, particularly cryo-electron tomography, is highly desirable.

Knowledge of causal inference methods and their application to complex biological systems will further strengthen the candidate’s profile.

Designs, develops, documents, tests, and…