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Data Science Machine Learning Internship (Summer 2027)
A full-time summer 2027 internship in London with Castleton Commodities International, focusing on machine learning and data science applications for commodity market insights.
London
Undergraduate
Energy Commodities, Data Science, Technology
In-Person
1 September 2026
Last verified: 10 October 2026
About this opportunity
The Data Science Machine Learning Internship at Castleton Commodities International is a full-time summer 2027 opportunity based in London. Interns will join the Global Data Science team, analyzing time series data and applying machine learning to extract market insights in the power, natural gas, and oil sectors. Responsibilities include implementing and optimizing ML models, designing data ingestion processes, and conducting research to support commodity trading decisions.
Who is it for?
Undergraduate students expected to graduate in Winter 2027 or Spring/Summer 2028, pursuing degrees in Mathematics, Statistics, Physics, Computer Science, or related fields with a focus on Machine Learning.
What you will gain
Hands-on experience in machine learning applied to commodity markets, collaboration with experienced data scientists and trading professionals, and the opportunity to work on real-world forecasting and analytics projects.
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Requirements
Currently pursuing a Bachelor's degree or higher in a quantitative or technical field related to machine learning; expected graduation date Winter 2027 or Spring/Summer 2028; experience with machine learning techniques and Python programming; strong analytical and communication skills.
How to apply
Apply through CCI’s Workday careers portal before the deadline.
Applications closed
This opportunity is no longer accepting applications. Browse similar opportunities that are open now.
Find similar open opportunitiesAlways confirm eligibility, deadlines, and requirements directly on the official Castleton Commodities International website before applying. Nentra does not guarantee the accuracy of third-party programme details.
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