Data Science Machine Learning Internship (Summer 2027) at Castleton Commodities International LLC

Castleton Commodities International (CCI) is a leading global energy commodities merchant and infrastructure asset investor. As a trader, CCI deploys capital on a proprietary basis in the physical and financial commodity markets. As a strategic investor and developer, CCI leverages its market expertise, operations capabilities, and industry knowledge to invest in and develop select commodity infrastructure assets.

About the role

CCI is developing a leading-edge Data Science platform to stay at the forefront of data management and analytics, essential to our investment strategy. We are looking for motivated and detail-oriented Machine Learning Interns with a strong interest in quantitative analysis, particularly time series forecasting, to join our Global Data Science team in Stamford, CT, Houston, TX, or New York City offices. This internship provides a unique opportunity to work with fundamental market data, generating insights that support our commercial trading business.

Responsibilities

• 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.

Qualifications

• 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.