Data Scientist II - Mathematician​/Statistician

Overview

At PNNL, our core capabilities are divided among major departments referred to as directorates within the Lab, focused on specific scientific research or other functions, 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 address complex, real‑world threats to our nation and the world. The AI and Data Analytics Division, part of NSD, combines domain expertise and advanced hardware/software to deliver computational solutions addressing complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing 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.

PNNL is seeking a Data Scientist II – Mathematician / Statistician who has knowledge of the fundamentals of mathematics and statistics in one or more areas including artificial intelligence, operations research, modeling of complex systems or events, risk analysis, scientific machine learning, and optimization.

The selected candidate will contribute to research and development programs within the AI and Data Analytics Division which specializes in data science, applied mathematics, advanced analytic architectures, software engineering, and human‑centered computing. The Data Scientist will work as part of interdisciplinary teams to deliver data‑driven solutions that address critical national security challenges. The candidate will support PNNL's mission by contributing to impactful R&D projects that help tackle national challenges.

Successful candidates will have the opportunity to grow professionally while working on diverse, mission‑focused projects. At PNNL, we foster a collaborative and innovative work environment aimed at lifelong learning, creative problem‑solving, and advancing interdisciplinary, data‑driven innovation.

This position is based in either Richland, WA or Seattle, WA and requires an onsite presence 4 days/week.

Key Responsibilities:

• Develop mathematical or statistical models of data and algorithms to analyze those models. Execute experiments to gather insight into the data and systems that generate the data. Use artificial intelligence approaches where appropriate.
• Ensure experiments are statistically sound, including standard performance metrics, and results are reproducible. Assess empirical results to guide future research and experimental steps.
• Maintain awareness of emerging trends in mathematics and statistics for data science challenges in national security to shape future research directions and identify new opportunities for impact.
• Write high‑quality research code demonstrating proficiency with data science techniques and tools.
• Communicate effectively with team members, project leadership, management, and stakeholders.
• May require conducting work in secure environments and executing work from an operational security perspective.

Minimum Qualifications:

• BS/BA and 2 years of relevant experience – OR – MS/MA – OR – PhD

Preferred Qualifications:

• Degree in mathematics, statistics, data science, or related fields.
• Familiarity with current research landscape in fundamentals of mathematics and statistics in one or more areas including artificial intelligence, operations research, modeling of complex systems or events, risk analysis, scientific machine learning, and optimization.
• Awareness of and willingness to learn domain‑specific knowledge in specialty domains, e.g., communications, geospatial data, nuclear nonproliferation, radiography, remote sensing.
• Passion for agentic AI and large language models, including experience using or developing these tools in various data science domains.
• Int…