Data Science Internship
Listed on 2026-09-12
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IT/Tech
Data Scientist, Machine Learning/ ML Engineer, Data Analyst
Description Internship Overview
You won't be running coffee orders or shuffling paperwork this summer. At Fervo, interns are handed something real: a project of your own, scoped with your manager on day one and yours to drive for the full 12 weeks. You'll work side-by-side with the teams building the next generation of geothermal energy, tackling problems that genuinely move the business forward. At the end of the summer, you'll present your work to our executive leadership team, department leads, and fellow interns, sharing real results with a real audience.
This is a real seat at the table — and a real shot at what comes next.
Fervo Energy is developing next-generation geothermal power to deliver firm, carbon-free energy at scale, anchored by our flagship Cape Station development in Milford, Utah. As a Data Scientist Intern, you'll join Fervo's Data Science team to help build models and analyses that turn data into decisions across the business.
You'll work alongside engineers and data scientists to explore datasets, build predictive models, and translate findings into insights that inform real decisions across drilling, operations, and commercial teams.
Requirements Responsibilities- Explore and analyze datasets to identify trends and opportunities
- Build and validate predictive models and statistical analyses
- Support development of machine learning models for real-world business problems
- Communicate findings and recommendations to technical and non-technical stakeholders
- Master's or PhD candidate wrapping up within the next year — we're building a pipeline toward full-time offers
- Pursuing a degree in Data Science, Statistics, Computer Science, or a related quantitative field
- Strong written and verbal communication skills, including comfort presenting to stakeholders and leadership
- Eagerness to learn, take initiative, and adapt quickly to new challenges
- Proficiency in Python and common data science libraries (e.g., Pandas, Num Py, Scikit-learn)
- Experience with machine learning frameworks (e.g., PyTorch, Tensor Flow)
- Familiarity with SQL and data visualization tools
- Interest in applying data science to energy or industrial problems
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