Data Scientist 1

Pacific Northwest National Laboratory is hiring a Data Scientist 1 for the Earth Systems Science Division in Lincoln, NE. The role involves applying data science techniques to large-scale datasets, developing machine learning models, and creating reproducible workflows for energy and environmental research. Responsibilities include data preparation, modeling, visualization, collaboration with multidisciplinary teams, and continuous learning in data science practices. This position is ideal for early-career professionals.
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?Energy and Environment Directorate?delivers?science and technology solutions for the nation's biggest energy and environmental challenges. Our more than 1,700 staff support the Department of Energy (DOE), delivering on key DOE mission areas including: modernizing our nation's power grid to maintain a reliable, affordable, secure, and resilient electricity delivery infrastructure; research, development, validation, and effective utilization of renewable energy and efficiency technologies that improve the affordability, reliability, resiliency, and security of the American energy system; and resolving complex issues in nuclear science, energy, and environmental management.

The?Earth Systems Science Division, part of the Energy and Environment Directorate, provides leadership and solutions that advance Earth system opportunities for energy systems and national security. We are a multidisciplinary division connected by a shared commitment to innovate and collaborate towards solving complex problems in the dynamic Earth system.

Responsibilities

Pacific Northwest National Laboratory (PNNL) is seeking a Data Scientist 1 to join the Applied Decision Systems & Analytics Group within the Earth Systems Science Division (ESSD) . This team develops advanced data analytics, modeling, and computational solutions to address complex challenges in energy systems, environmental modeling, andearthsciences.

As a Data Scientist 1, you will apply data science techniques to large-scale scientific datasets, develop machine learning models, and create reproducible workflows that support research in climate, energy, and environmental domains. This is an excellent opportunity for early-career professionals to contribute to impactful projects that advance national energy and environmental goals.
• Data Preparation & Analysis: Clean, transform, and integrate structured and unstructured datasets from environmental and energy systems domains.
• Modeling & Algorithms: Implement supervised and unsupervised learning methods (classification, regression, clustering) using industry-standard libraries.
• Visualization & Reporting: Build effective visualizations and dashboards; communicate findings to technical and non-technical stakeholders through clear narratives and reproducible reports.
• Software & Reproducibility: Develop well-structured, documented code; use version control (Git) and adhere to reproducible research practices.
• Collaboration: Work within multidisciplinary teams; contribute to project planning, documentation, and regular status updates.
• Continuous Learning: Stay current with data science tools, libraries, and best practices; adopt laboratory standards for security, data stewardship, and scientific integrity.

Qualifications

Minimum Qualifications:
• BS/BA or higher

Preferred Qualifications:
• Bachelor's degree in Data Science or Computer Science
• Technical Skills: Foundational experience with at least one high-level programming language (e. g. , Python or R) for data analysis/modeling, plus SQL for data access.
• Core Knowledge: Understanding of statistical concepts (e. g. , distributions, hypothesis testing) and basic machine learning workflows (data splitting, evaluation metrics, model validation).
• Communication: Ability to present analysis clearly, write concise documentation, and collaborate in diverse teams.
• Experience with SQL, database management systems, and numerical/scientific computing libraries such as NumPy and SciPy.
• Experience developing software for data analysis, statistical workflows, and system quality engineering, including work with older or legacy codebases while preserving functionality and improving quality.
• Familiarity with version control tools (e. g. , Git) and modern front end frameworks such as ReactJS, Svelte, or Angular.
• Strong data wrangling, cleaning, and preprocessing skills, with the ability to produce clear visualizations using tools like Matplotlib, Seaborn, Plotly, or Tableau.
• Understanding of supervised and unsupervised machine learning techniques and experience with frameworks such as scikit learn, TensorFlow, or PyTorch.
• Knowledge of statistical methods, hypothesis testing, and experimental design.
• Exposure to big data technologies (e. g. , Spark, Dask) and cloud platforms such as AWS, Azure, or GCP.
• Experience applying data science or machine learning methods to scientific research, particularly in energy or environmental domains.
• Understanding of data provenance frameworks to ensure traceability and reproducibility in large scale workflows.
• Familiarity with AI technologies and their application to code refactoring, generation, and enhancement to improve software reliability and structure.
• Ability to communicate effectively with both technical and non technical audiences and to contribute in multidisciplinary research teams.
• Coursework or certifications in data science, machine learning, or statistics.
• Familiarity with DOE or national laboratory research environments.
• Understanding of key concepts related to nuclear incident preparation and response.
• Curiosity and a problem-solving mindset with attention to detail and data quality.
• Commitment to scientific integrity, reproducibility, and responsible AI/data use.
• Flexibility to learn new domains, tools, and methods quickly.
• Collaboration-first approach with strong organizational skills.
• Capacity to work autonomously on technical challenges.

Hazardous Working Conditions/Environment

Not Applicable

Additional Information

This position requires the ability to obtain and maintain a federal security clearance.

A security clearance background investigation includes review of your employment, education, financial, and criminal history, as well as interviews with you and your personal references, neighbors, and co-workers to determine trustworthiness, reliability, and loyalty to the United States. The investigation also examines your foreign connections, drug and alcohol use, foreign influence, and overall conduct.

•