Working at NREL
The National Renewable Energy Laboratory (NREL), located at the foothills of the Rocky Mountains in Golden, Colorado is the nation’s primary laboratory for research and development of renewable energy and energy efficiency technologies.
From day one at NREL, you’ll connect with coworkers driven by the same mission to save the planet. By joining an organization that values a supportive, inclusive, and flexible work environment, you’ll have the opportunity to engage through our eight employee resource groups, numerous employee-driven clubs, and learning and professional development classes.
NREL supports inclusive, diverse, and unbiased hiring practices that promote creativity and innovation. By collaborating with organizations that focus on diverse talent pools, reaching out to underrepresented demographics, and providing an inclusive application and interview process, our Talent Acquisition team aims to hear all voices equally. We strive to attract a highly diverse workforce and create a culture where every employee feels welcomed and respected and they can be their authentic selves.
Our planet needs us! Learn about NREL’s critical objectives, and see how NREL is focused on saving the planet.
Note: Research suggests that potential job seekers may self-select out of opportunities if they don’t meet 100% of the job requirements. We encourage anyone who is interested in this opportunity to apply. We seek dedicated people who believe they have the skills and ambition to succeed at NREL to apply for this role.
Job Description
The Geospatial Data Science (GDS) Group in NREL’s Strategic Energy Analysis Center is seeking a research scientist to conduct analysis of spatial and temporal data to help solve real-world energy system problems and advance renewable energy deployment. The GDS Group conducts research at the intersection of renewable energy deployment, big data science, and geospatial modeling and visualization. Our team of scientists develops and applies geospatial algorithms and methods to evaluate the resource, technical, and economic potential for renewable energy technologies, including wind, solar, geothermal and bioenergy. Our visualization scientists put modeling results and actionable insights in the hands of industry partners, decision-makers, and the broader public through state-of-the-art geospatial web applications. Geospatial modeling at NREL enables detailed techno-economic assessment and power systems modeling of renewable energy resources under a variety of regulatory, sociopolitical, and environmental factors from local to continental scales.
This position will support the growing research portfolio of the GDS Group, creating new capabilities to analyze scenarios and design solutions for complex challenges in renewable energy. The position will support clients within NREL and external to the laboratory. The successful candidate will develop novel analytical solutions to advance the state of research in broad geographic scale representation of spatial drivers of renewable energy deployment and the evolution of our power system. This position requires a strong applied and theoretical background in spatiotemporal methods applied in social, ecological, and topographical modeling contexts, as well as understanding of complex systems analysis and scenario modeling frameworks.
Duties will include:
- Integrating multiple data sources, models, and software tools with scientific and engineering workflows for data analysis and decision support. These workflows will include the use of distributed parallel computing and utilization of both cloud and high-performance computing (HPC) resources.
- Conduct and lead analysis using the Renewable Energy Potential (reV) model to develop wind and solar supply curves.
- Ideating and evaluating novel research strategies and methods to uncover insights and inform sustainable energy solutions at various geographic and temporal scales.
- Communicating research insights through journal publications and conference presentations; participating in stakeholder engagement workshops and seminars; and contributing on proposals for new research directions.
- Technical oversight and task management to meet client expectations while managing timelines, deliverables, and budgets.
The ideal candidate will bring deep background and experience in spatial analysis and methods development, quantitative research, and advanced programming skills to integrate models with large data sets (i.e., >1 TB). Experience with Linux-based, scalable, open-source analysis tools is required. Expert proficiency in geospatial modeling is required, although experience with ESRI products is not a preferred skill due to computational demands and highly customized solutions developed by the group. The candidate should be eager to work in an interdisciplinary field, together with computer scientists, policy analysts, and system engineers, and will require excellent interpersonal and communication skills.
Specific required skills include:
- Strong quantitative background in the areas of spatial statistics, predictive modeling, data analytics, and/or complex systems modeling.
- Strong scientific programming and algorithm development skills and demonstrated use of Python and Postgres/PostGIS for modeling and analysis of large, complex data sets.
- Demonstrated ability to communicate results effectively through scientific visualizations, publications, and presentations.
The NREL Geospatial Data Science group (https://www.nrel.gov/gis/) is the pre-eminent geospatial team working in renewable energy research. Our cutting-edge research and modeling support diverse programs across NREL and beyond, advancing renewable energy solutions from local to global scales. Join our team as we develop the science placing renewable energy on the map.
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Basic Qualifications
This Researcher can be hired at a level II or level III:
Researcher III:
PhD in Engineering, Geography, Environmental Science, Computer Science or related. Or, Master’s Degree in Engineering, Geography, Environmental Science, Computer Science or related and 3 or more years of experience. Or, relevant Bachelor’s Degree in Engineering, Geography, Environmental Science, Computer Science or related and 5 or more years of experience. Demonstrates complete understanding and wide application of scientific technical procedures, principles, theories and concepts in the field. General knowledge of other related disciplines. Demonstrates leadership in one or more areas of team, task or project lead responsibilities. Demonstrated experience in management of projects. Very good technical writing, interpersonal and communication skills.
Researcher II:
Master’s Degree in Engineering, Geography, Environmental Science, Computer Science or related. Or, relevant Bachelor’s Degree in Engineering, Geography, Environmental Science, Computer Science or related and 2 or more years of experience. General knowledge and application of scientific technical standards, principles, theories, concepts and techniques. Training in team, task or project leadership responsibilities. Intermediate abilities and knowledge of practices and techniques. Beginning experience in project management. Good technical writing, interpersonal and communication skills.
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Additional Required Qualifications
- Demonstrated experience in uncertainty quantification in relevant research domains.
- Theoretical background and applied experience with machine learning models in a spatial context.
- Knowledge in renewable energy domains with a particular focus on wind and solar technologies.
- Understanding of power systems models (e.g., integrated assessment, capacity expansion, or production cost models).
Preferred Qualifications
- Demonstrated leadership as a technical lead and project manager.
- Broad understanding and application of scientific technical procedures, theories, and principles in applied geospatial research.
- Demonstrated experience in uncertainty quantification in relevant research domains.
- Extensive knowledge in renewable energy domains with a particular focus on wind energy technologies.
- Background in capacity expansion modeling, production cost modeling and renewable energy resource assessment.