Forecasting and Optimization Engineer

About the Opportunity

Enabling millions of distributed energy devices to deliver reliable and predictable grid services to the electricity industry requires state-of-the-art forecasting, control systems, optimization algorithms, and data science. As such, EnergyHub seeks to hire a Senior Forecasting and Optimization Engineer to join EnergyHub’s Data Science and Power Systems Intelligence team. As a Senior Forecasting and Optimization Engineer you will work closely with some of the brightest minds in the electricity industry to solve some of the most difficult problems in distributed energy. The algorithms and data systems that you contribute to will be key to enabling a carbon-free distributed energy future.

Main Responsibilities: What you’ll do

• Develop and improve accurate and robust energy forecasting models
• Develop and improve control and optimization algorithms
• Design, write, test, and deploy production-grade code for mission-critical forecasting and optimization products and related algorithms
• Build and use tools and processes to monitor and assess the quality of operational forecasting and optimization models
• Translate data from one of the largest distributed energy datasets in the electricity industry into actionable insights for electric utilities and other energy industry stakeholders
• Provide guidance to other engineers and scientists regarding machine learning, forecasting and optimization methods
• Present results clearly to internal and external stakeholders
• Collaborate closely with stakeholders inside and outside of EnergyHub including Data Engineering, Client Success, Marketing, Sales, Product, and Market Development teams
• Other duties as assigned

Required Skills and Experience: What you need

• Passion for enabling a carbon-free, distributed energy future
• At least five years of experience in machine learning, statistical analysis, data visualization, control systems engineering, and/or mathematical optimization
• Experience developing data pipelines using large-scale relational databases
• Strong mathematical skills, with the ability to apply new mathematical techniques to real-world problems
• At least three years of hands-on experience with Python and the scientific and machine learning tool stack (Pandas, Numpy, SciPy, Sklearn, PyTorch, and Tensorflow)
• An ability to find valuable insights in large, complex datasets
• Ability to explain results from large-scale data analyses to diverse audiences in a way that is clear, interesting, and actionable