Data Scientist II - Power System Operations

Data Scientist II - Power System Operations at Pacific Northwest National Laboratory in Boise, Idaho, United States Job Description Overview The Electricity Infrastructure and Buildings Division of PNNL is accelerating the transition to a decarbonized, efficient, resilient, and secure energy system through basic and applied research. We leverage a strong technical foundation in power and energy systems and in advanced data analytics to drive innovation, transform markets, and shape energy policy. This position will provide support to the Laboratory's research in Optimization and Control of Energy Systems. This position will focus on research at the intersection of controls, learning, and computing with an emphasis on data driven and learning based control methods for enhancing the efficiency of energy systems including the electric power grid, buildings, and transportation systems. A successful candidate will be required to conduct independent research, develop new ideas and contribute to research proposals, engage with business development managers in relevant areas, prepare manuscripts for publication, and present their work to clients and at conferences. Responsibilities + Providing technical input on large, complex, formal documents, such as project reports, lead author on journal articles, conference papers, and presentations. + Selecting and developing technical approaches to project work assignments. + Contributing to technical proposals for developing new projects and business. + Working with senior staff and project managers to develop and implement project management plans for moderate to large projects. + Working in a team environment of high-performance multi-disciplinary experts. + Accountable to team leader, group leader and project managers for project development, execution, and closure. Qualifications Minimum Qualifications: + BS/BA and 2 years of relevant experience OR + MS/MA OR + PhD Preferred Qualifications: + Strong technical background in statistical methods, reinforcement learning, uncertainty quantification. + Good understanding of and interest in advanced grid modeling techniques, renewable energy systems, and energy policy. + Research experience in demand response, energy storage and other distributed energy resources. + Proficiency in python, R, and excel. + Familiar with existing deep learning libraries (e.g., TensorFlow, PyTorch, Caffe2) and machine learning packages (i.e. sklearn). + Experience applying machine and deep learning or statistical methods to domain specific applications, such as buildings and power grid. To view full details and how to apply, please login or create a Job Seeker account