Description
National Grid Renewables, which includes the renewables development company formerly known as Geronimo Energy, is a leading North American renewable energy company based in Minneapolis, Minnesota, with satellite offices located in the regions where it develops, constructs, and operates renewable energy projects. As a farmer-friendly and community focused company, National Grid Renewables develops projects for corporations and utilities that seek to repower America's electricity grid by reigniting local economies and reinvesting in a sustainable future. National Grid Renewables is part of the competitive, unregulated Ventures division of National Grid and has a portfolio of solar, wind, and energy storage projects located throughout the United States in various stages of development, construction and operation.
National Grid Renewables develops high value, competitive renewable energy projects. Our focus on communities and farmers means it's not just about projects, but about the people we work with, both outside and inside our organization. National Grid Renewables Team Members embody our foundational culture of being entrepreneurial, creative, and nimble and take pride in supporting National Grid's vision to be at the heart of a clean, fair, and affordable energy future for all.
About the Position
National Grid Renewables is hiring a Data Scientist to improve our suite of machine learning products. The ideal Data Scientist will be an individual who enjoys understanding and improving large coding problems. They will be skilled in machine learning, math, statistics, and computer science, and have a passion for clean energy. This opportunity may be remote, with a preference of hybrid work out of the Bloomington, MN corporate office location.
Role Responsibilities:

• Forecasting model development: Improve quality of forecasts for the suite of energy market products at operating power plants.
• Uptime Optimization: Increase the uptime of the forecast engines.
• Market choice algorithm: Contribute to and improve the market choice algorithm for solar, wind, and battery power plants.
• Integration with real-time power plant data: Collaborate with asset management, engineering, and field operation teams to ensure real-time physical data is being represented in market choice algorithms.
• Code reviews and pull-requests: Review and document code to maintain best practices for organization.
• Performance evaluation: Conduct thorough evaluation and testing of machine learning models and optimization algorithms using historical data and simulation scenarios.
• Research and innovation: Stay updated with the latest advancements in machine learning and energy price forecasting.

What you bring to this role:

• Candidate for an undergraduate or graduate degree in degree in Computer Science, Data Science, Electrical Engineering, Social Science or quantitative fields.

• Programming Proficiency: Strong programming skills in Julia (preferred), Python, or R. Familiarity with machine learning libraries like Scikit-learn, TensorFlow, or PyTorch.

• Machine Learning Knowledge: Solid understanding of various machine learning algorithms, data preprocessing techniques, and model evaluation methods.

• Energy Domain: Basic knowledge of energy markets, electricity pricing, and grid operations is a plus but not required.

• Problem-Solving Abilities: Analytical mindset with the ability to tackle complex problems and find innovative solutions.

• Team Player: Strong collaboration skills, with the ability to work effectively in a team-oriented environment.

• Communication Skills: Excellent verbal and written communication skills. Capable of explaining technical concepts to non-technical stakeholders.

• Learning Attitude: Eagerness to learn and stay updated with emerging technologies and industry trends.