Data Scientist II - Mathematician / Statistician

Pacific Northwest National Laboratory•Richland, WA

8d•Hybrid

About The Position

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.

Requirements

BS/BA and 2 years of relevant experience -OR- MS/MA -OR- PhD
U.S. Citizenship
Ability to obtain and maintain a federal security clearance.
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.
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).
Mandatory Requirements: The Department of Energy (DOE) prohibits DOE employees and contractors from having any affiliation with the foreign government of a country DOE has identified as a “country of risk” without explicit approval by DOE and Battelle.

Nice To Haves

Degree in mathematics, statistics, data science, or related fields.
Familiarity with the current research landscape in 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.
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.
Interest in working across the spectrum from research to prototype development to deployable tools.
Proficiency with data science techniques and tools including cloud computing platforms and version control systems like git, and flexibility in working with various systems and environments including on-premises servers and cloud platforms.

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.