Ph.D. Research Internship – Data Science & Machine Learning (Summer 2026)

Castleton Commodities International 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, providing the Company with market insights and access. 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. Our strategically integrated platform has generated strong risk-adjusted returns for our investors since our formation.

Application Deadline: September 14th, 11:59pm EST 

Program Overview Video

Position Overview:

CCI is developing a leading-edge Data Science platform, as staying at the forefront of data management and analytics is essential to our investment strategy. We are seeking a highly motivated and detail-oriented Ph.D. Machine Learning Intern within our Global Data Science team, based in our Stamford, CT or London office. Ideal candidates will have a strong interest in applied quantitative research, particularly in time series forecasting, and a passion for developing novel machine learning methodologies to address real-world challenges in dynamic markets. This role offers the opportunity to conduct impactful research, collaborate with experienced scientists, and contribute to the advancement of our data-driven forecasting capabilities. The Ph.D. Machine Learning Internship provides a unique opportunity to work with fundamental market data, generating insights that support our commercial trading business. You will be responsible for analyzing time series data related to market fundamentals in the Power, Natural Gas, and Oil sectors, helping to identify key supply and demand drivers. These insights will play a vital role in forecasting price movements and supporting risk management decisions.

Responsibilities:

• Conduct research on emerging project topics, including energy fundamental data, analytics trends, and best practices in big data and artificial intelligence.

• 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.

Qualifications:

• Currently pursuing a Ph.D. in Statistics, Physics, Computer Science, Mathematics, or related technical field with a focus in Machine Learning.

• Expected graduation date of Winter 2026 or Spring/Summer 2027.

• 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.

Visit  https://www.cci.com/careers/life-at-cci/# to learn more!

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