Commodities Quantitative Research Extern, Rice University

Jain Global•Houston, TX

About The Position

Jain Global is seeking a Quantitative Research Extern to work alongside our commodities investment teams. The role offers direct exposure to commodity markets and the investment process, with an emphasis on applying quantitative methods, fundamental analysis, and data-driven research to real-world trading problems. Externs will work closely with Portfolio Managers, traders, and analysts to investigate market behavior, develop and test investment hypotheses, build analytical tools and models, and evaluate new datasets. The role is highly project-oriented and provides an opportunity to take ownership of research from initial idea through analysis, back testing, and communication of results. You will focus on commodities markets such as crude oil, refined products, natural gas and power, combine complex physical systems, rapidly evolving fundamentals, large and often noisy datasets, and liquid financial markets. Successful externs will bring intellectual curiosity, quantitative rigor, and a willingness to develop a deep understanding of the economic and physical drivers behind the data.

Requirements

Strong quantitative ability, intellectual curiosity, and an interest in applying rigorous research to financial and commodity markets.
Eligible students enrolled at Rice University.
Current enrollment in a bachelor's, master's, or PhD program in a quantitative discipline such as mathematics, statistics, computer science, engineering, physics, economics, or a related field
Strong quantitative, analytical, and problem-solving skills
A solid foundation in probability, statistics, and statistical modeling
Experience working with real-world datasets and an understanding of issues such as noise, missing data, overfitting, model assumptions, and out-of-sample testing
Programming experience in Python
Demonstrated experience working with and analyzing data using SQL, Python data tools, and/or Microsoft Excel
Familiarity with one or more areas such as time-series analysis, econometrics, optimization, machine learning, stochastic modeling, or derivatives
The ability to approach open-ended research questions independently and develop a structured framework for investigating them
Strong attention to detail and a high standard for analytical rigor
The ability to communicate complex quantitative ideas clearly and concisely
A collaborative mindset and the ability to operate effectively in a fast-paced, performance-oriented environment
A curiosity for financial markets, commodities, and the interaction between quantitative models and real-world market behavior

Nice To Haves

Experience with C++, R, or similar languages is also valuable
Previous experience in finance, commodities, or energy markets is helpful but not required.

Responsibilities

Work directly with Portfolio Managers, traders, and analysts focused on specific commodities markets, such as North American natural gas and power markets.
Conduct independent, project-based quantitative research using large and complex datasets.
Develop hypotheses about market behavior and use statistical analysis, modeling, and back testing to evaluate them.
Build and evaluate quantitative models used to understand market dynamics, forecast key variables, identify trading opportunities, or assess risk.
Apply statistical, econometric, machine learning, and time-series techniques while understanding the assumptions, limitations, and robustness of each approach.
Analyze new and alternative datasets to determine whether they can improve existing forecasts, signals, models, or research workflows.
Translate research ideas into reproducible code, analytical tools, and research infrastructure.
Evaluate model and strategy performance across different market environments and investigate sources of performance or model failure.
Combine quantitative analysis with an understanding of physical market fundamentals to develop differentiated market insights.
Present research findings, market observations, and model results clearly and concisely to Portfolio Managers and other members of the investment team.
Challenge existing assumptions, propose new approaches, and take ownership of ideas that can improve the team's investment process.