Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International is a leading global energy commodities merchant and infrastructure asset investor. They are looking for motivated and detail-oriented Machine Learning Interns to join their Global Data Science team, focusing on analyzing time series data related to market fundamentals in the Power, Natural Gas, and Oil sectors.

What you'll do:

• Apply mathematical and statistical knowledge to enhance existing machine learning applications and explore new solutions
• Work closely with Data Scientists, Analysts, and Traders to design, implement, and optimize machine learning models for time series forecasting, including ARIMA/SARIMA, gradient boosting methods (e.g., XGBoost), LSTM networks, and linear regression-based approaches
• Assist in designing and implementing end-to-end data ingestion processes, ensuring seamless data flow to investing teams
• Work with desk heads, traders, and analysts to understand current data architecture, investment processes, and functional requirements for data science analysis
• Contribute to identifying and back-testing new data sets, leveraging machine learning techniques to drive insights
• Conduct ad hoc research on emerging project topics, including energy fundamental data, analytics trends, and best practices in big data and artificial intelligence

What they're looking for:

• Currently pursuing a Bachelor's Degree or higher in Mathematics, Statistics, Physics, Computer Science or related technical field with a focus in Machine Learning
• Expected graduation date of Winter 2027 or Spring/Summer 2028
• Experience applying machine learning techniques such as regression, time series forecasting, deep learning, reinforcement learning, or predictive modeling to solve problems involving complex data patterns and market dynamics
• Strong programming experience in Python (preferred libraries: Pandas, NumPy, etc.)
• Ability to communicate and interact with a wide range of users, from very technical to non-technical backgrounds
• Strong analytical skills with demonstrated attention to detail