NREL Data Scientist for Energy Systems in Golden, Colorado
Data Scientist for Energy Systems
CO - Golden
Limited Term (Fixed Term)
Hours Per Week
The Data, Analysis and Visualization Group in NREL’s Computational Science Center is seeking a full-time (40 hours per week) year-round professional data scientist for energy systems. The candidate will work with staff scientists on diverse data science and data management projects including transportation and mobility analytics, wind turbine and gearbox prognostics, cloud data management and potentially additional projects depending on needs as they arise . These projects involve a mixture of data management and curation, scientific programming, model building and analysis. The candidate will make use of existing datacenter systems including Kafka data streaming, Elasticsearch clusters, Spark/Hadoop clusters and our High Performance Computing (HPC) cluster (Eagle) to solve problems at scale, as well as cloud-based resources for collaborative data science.
Interested candidates should have experience programming python (optionally, in addition to other languages), accessing APIs and databases (SQL), some background or experience with machine learning and applied predictive modeling, as well as a high degree of curiosity, willingness to learn to skills and ability to adapt to the data needs of differing domains. Successful applicants will also have some prior experience with parallel and distributed programming and/or big data frameworks and scientific plotting libraries (e.g., plot.ly, matplotlib) in a High Performance Computing environment. All applicants should have a strong familiarity with Unix/Linux operating systems, cloud computing services, and open source software.
Specific duties will include programming, both for data management and organization (ETL), as well as modeling. Technical project management and the ability to lead and consult on complex projects with numerous contributors is a plus. Candidates should be able to demonstrate some existing skills and experience in applying data science in industry, academia, or government settings.
Master's Degree in Computational and/or Applied Mathematics or related degree. Or, Bachelor's Degree in Computational and/or Applied Mathematics (or related degree) 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. Good technical writing, interpersonal and communication skills.
Familiarity with foundational statistical concepts such as regression models, uncertainty quantification, Bayesian analysis, model selection, clustering, outlier detection, etc.
Familiarity with GIS software, systems and databases and experience manipulating and analyzing GIS data.
Experience in big data analytics on diverse, asynchronous data sets including experience in designing efficient and robust ETL workflows.
Sufficient software engineering expertise to enable big data solutions: object-oriented design, coding and testing patterns; engineering software platforms and large-scale data infrastructures.
Experience using deep learning frameworks (e.g., TensorFlow) on high performance computing/GPU platforms.
Experience with parallel and distributed programming, big data frameworks, notably including streaming data systems (e.g., Kafka), and scientific plotting libraries (e.g., plot.ly, matplotlib).
A strong familiarity with Unix/Linux operating systems and open source software.
Background in relevant engineering disciplines esp. of transportation systems, vehicle technologies, and/or power systems.
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