Summer Intern, Quantitative Analytics

About the position Cypress Creek Renewables is powering a sustainable future, one project at a time. We develop, finance, own and operate utility-scale and distributed solar and storage projects across the country. Fostering a diverse group of innovative thinkers from all backgrounds, Cypress people are drawn to work in a purpose-driven organization. We hope you will join us.

Overview

Cypress Creek Renewables (CCR) is a leading renewables Independent Power Producer (IPP). We develop, finance, own and operate utility-scale and distributed solar and battery storage projects across the country. Our mission is to power a sustainable future, one project at a time. Since inception, CCR has developed more than 11GW of solar projects. Today we own 2GW of solar and through our O&M Services business, we operate 5GW of solar projects, the fourth largest provider in the US. Fostering a diverse group of innovative thinkers from all backgrounds, CCR people are drawn to work in a purpose-driven organization. We hope you will join us. The Quantitative Analytics Team at Cypress Creek is responsible for all the analytics that goes into revenue optimization and risk mitigation of the Company’s development pipeline and operating portfolio of solar and storage projects. The Quantitative Analytics Team is seeking graduate (Master’s or PhD) candidates for a 10-12 week, paid internship in one of our office locations or remotely. We are looking for candidates with a strong knowledge in one or more of the following areas - machine learning, stochastic optimization, model predictive control, or reinforcement learning. Domain expertise in energy markets is preferred but not required. The Quantitative Analytics Team has a strong desire to develop the intern who joins our team, with the hope of this opportunity leading to a full-time offer upon graduation. Some sample projects you may work on depend on the experience of the candidate and include: Developing a prototype of a real-time battery optimization algorithm utilizing stochastic price forecasts Developing a comprehensive MLOps framework for price forecasting models Researching and developing DA/RT and Point-to-Point (PTP) trading algorithms and risk adjusted performance metrics

Responsibilities

• Developing a prototype of a real-time battery optimization algorithm utilizing stochastic price forecasts
• Developing a comprehensive MLOps framework for price forecasting models
• Researching and developing DA/RT and Point-to-Point (PTP) trading algorithms and risk adjusted performance metrics

Requirements

• Must be in grad school (Masters or PhD)
• Prefer computer science, statistics, or analytics degrees or course work (include examples in cover letter or resume)
• Must be proficient in either Python or Julia
• Please discuss your interest in energy analytics, and how you have taken steps to become more involved in (or deepen your understanding of) the industry during the past few years?

Nice-to-haves

• Prefer a basic understanding of energy market operations
• Ideally have a strong desire to work in clean energy / clean tech post college
• Domain expertise in energy markets is preferred but not required.

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