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

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.

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