Job description
The Company
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
Qualifications:
- 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
- Prefer a basic understanding of energy market operations.
- Ideally have a strong desire to work in clean energy / clean tech post college.
In the cover letter, please address the following question:
- 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?
Locations: The preferred location for this role is one of our offices. We also have offices in Durham, NC; Washington, DC; NYC; Chicago, IL, Asheville, NC; Los Angeles, CA; and San Francisco, CA. Additionally, we are on a hybrid schedule 3/week.
A note to Recruiting Agencies Cypress Creek Renewables Human Resources team does not accept unsolicited resumes from third party recruiters, staffing firms, or related agencies. The Human Resources team coordinates all recruiting and hiring at our company. We do not accept resumes from third-party recruiters unless authorized by the Human Resources team and if a signed agreement is in place. Any unsolicited resumes will be considered property of CCR and we are not responsible for any related fees. All communication related to recruiting partnerships should ONLY be directed to the Human Resources team.
Cypress Creek Renewables is an equal opportunity employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status. We are committed to providing a workplace that is inclusive and values diversity, and we encourage candidates from all backgrounds to apply.
