Data Scientist III - Building Energy Applications

Overview

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 Electricity Infrastructure and Buildings Division, part of the Energy and Environment Directorate, is accelerating the transition to an efficient, resilient, and secure energy system through basic and applied research. We leverage a strong technical foundation in power and energy systems and advanced data analytics to drive innovation, transform markets, and shape energy policy.

Responsibilities

This position is in the Building Simulation and Design Group (BS&DG) within PNNL's Electricity Infrastructure and Buildings Division. BS&DG conducts modeling and analysis to evaluate the impacts of building energy policies, codes, and standards; develops tools and workflows to support building research and decision making; and helps accelerate adoption of energy-efficient technologies. The group maintains core research capabilities in building energy simulation, building energy policy analysis, and tool development for building applications.

The BS&DG is seeking a Data Scientist III - Building Energy Applications with strong programming and data analysis skills, advanced expertise in artificial intelligence and machine learning, and demonstrated ability to lead technical work. The selected candidate will serve as a hands-on technical lead. They will select and develop technical approaches and lead research applying data science and AI methods to building energy performance, building technologies, equipment, and energy efficiency policies, codes, and standards. Responsibilities include developing and improving analytical tools and reproducible data workflows, creating and maintaining data visualizations and infographics, guiding technical contributors, ensuring the technical quality of research results, and communicating findings to sponsors and stakeholders. The candidate will also develop and evaluate AI-enabled methods and tools for permitting modernization, predictive modeling, building-system controls, and research workflow automation, with an emphasis on accuracy, reproducibility, and practical value.

This position is based at a PNNL office in Seattle, WA, with an on-site presence expected. Hybrid work arrangements may be available in accordance with laboratory policy, project needs, and team expectations.

The successful candidate will be responsible for:

• Performing assigned roles under the direction of Project and/or Task Managers, following applicable project and field procedures, and completing assigned tasks on time and within budget.
• Defining research questions and selecting and developing data science and AI approaches to address building energy performance, technology, and codes and standards challenges, including appropriate methods, assumptions, evaluation metrics, and study boundaries.
• Organizing, integrating, and analyzing building and equipment datasets from field studies, operational measurements, surveys, product information, simulation results, and permitting and code-related documents.
• Designing and leading analytical studies to evaluate building technologies, equipment, energy efficiency policies, codes, and standards, including selecting appropriate baselines, comparing performance and energy use, and conducting sensitivity, scenario, and uncertainty analyses as appropriate.
• Developing, training, and evaluating statistical and machine learning models for building and equipment performance prediction, system identification, anomaly detection, and building-system controls, incorporating engineering knowledge and constraints in collaboration with relevant specialists.
• Developing and evaluating AI-enabled tools for permitting modernization and research workflow automation, including methods for extracting, organizing, and analyzing information from technical documents and supporting review processes.
• Developing reusable tools and improving reproducible workflows for data preparation, integration, quality checks, analysis, and reporting, including maintaining organized research datasets and collaborating with database and software developers.
• Establishing data validation and model-evaluation procedures, investigating data quality issues and unexpected findings, and assessing accuracy, robustness, analytical consistency, and reproducibility using appropriate benchmarks, reference data, statistical methods, and engineering knowledge.
• Creating and maintaining clear visualizations and updating the data underlying dashboards, infographics, and other research products.
• Leading major technical tasks or moderate projects, coordinating contributors, and managing assigned scope, schedule, budget, technical quality, and deliverables while remaining directly involved in programming, analysis, and method development.
• Working with sponsors, collaborators, and stakeholders to understand research needs, translate those needs into technical approaches, and communicate progress, findings, and limitations.
• Mentoring junior staff and reviewing analytical methods, models, code, and technical products developed by team members.
• Leading the development of technical reports, journal articles, and conference presentations, and representing project work in sponsor meetings and technical forums.
• Generating research ideas, developing funding opportunities with project teams, and leading technical sections of proposals.
• Collaborating across the group and division to share methods, strengthen research capabilities, and identify opportunities for related research.
• Maintaining accountability to Project and/or Task Managers, the Group Leader, and the Team Leader for assigned responsibilities, project execution, professional development, and operational discipline, including compliance with applicable procedures, training requirements, and quality, safety, and security expectations.

Qualifications

Minimum Qualifications:

• BS/BA and 5+ years of relevant work experience -OR-
• MS/MA and 3+ years of relevant work experience -OR-
• PhD with 1+ year of relevant experience

Relevant experience includes: graduate degree in data science, computer science, statistics, applied mathematics, electrical engineering, or a closely related field; strong programming and data analysis skills; advanced experience developing and evaluating AI or machine learning models for scientific or engineering applications; and experience independently developing technical approaches and leading technical tasks?

Preferred Qualifications:

• Graduate degree in data science, computer science, statistics, applied mathematics, electrical engineering, or a closely related field, with at least four years of relevant experience and demonstrated technical leadership and independent judgment in applied research.
• Strong programming and data analysis skills using Python or comparable tools, with experience developing reusable software and reproducible workflows for data preparation, quality assurance, analysis, and reporting.
• Advanced experience developing, training, and evaluating machine learning models for scientific or engineering applications, including selecting methods, establishing baselines and evaluation metrics, assessing uncertainty and limitations, and validating results.
• Experience incorporating physical relationships, engineering knowledge, or operational constraints into data-driven models, predictive methods, or optimization approaches for energy systems or related engineering applications.
• Experience developing and evaluating tools using large language models or other generative AI methods for technical information analysis, decision support, or research workflow automation. Experience applying these methods to permitting or code-related processes is a plus.
• Experience organizing and integrating datasets from multiple sources, investigating data quality issues, applying appropriate statistical methods, and developing defensible interpretations of results.
• Experience analyzing energy systems, building equipment, or related engineering systems in collaboration with domain specialists. Familiarity with building energy codes, equipment standards, or permitting processes is a plus.
• Experience creating and maintaining clear data visualizations and analytical interfaces, including dashboards, infographics, or other products that communicate complex findings. Familiarity with Tableau or comparable tools is a plus.
• Experience with version control, testing, documentation, and collaborative software development for research tools and analytical workflows. Experience with SQL, data pipelines, or scalable computing is preferred.
• Demonstrated experience selecting and developing technical approaches, leading technical tasks or projects, coordinating contributors, and delivering technically sound results within assigned scope, schedule, and budget while remaining directly involved in programming and analysis.
• Experience mentoring technical contributors and reviewing analytical methods, models, code, or research products developed by others.
• Strong written and verbal communication skills, with experience developing technical reports, publications, presentations, or research software; contributing technical content to proposals; and communicating effectively with sponsors, collaborators, and interdisciplinary teams.

Hazardous Working Conditions/Environment

Not Applicable

Testing Designated Position

This is not a Testing Designated Position (TDP).