Data Scientist III - Building Energy Applications

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 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