Commodities Quantitative Research Extern, Rice University

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.

Projects vary based on market conditions team priorities and the externs background and interests. Previous projects have included:

• Researching developing and back testing statistical arbitrage and relative-value strategies

• Conducting fundamentally driven trade research in various commodities markets

• Developing derivative pricing volatility and risk models

• Researching and implementing machine learning approaches for forecasting and signal generation

• Building time-series and predictive models for commodity prices and market fundamentals

• Evaluating new datasets and engineering features for use in investment models

• Developing analytics visualization tools and dashboards used in the research and trading process

Externs may work with datasets including futures and options prices market and order-book data real-time and day-ahead power prices power generation and load transmission and congestion data renewable generation forecasts natural gas fundamentals weather observations and forecasts and other commodity specific datasets.

• 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 teams investment process.

We are looking for candidates with strong quantitative ability intellectual curiosity and an interest in applying rigorous research to financial and commodity markets.

Candidates must be eligible students enrolled at Rice University.

Ideal candidates will have:

• Current enrollment in a bachelors masters 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; experience with C R or similar languages is also valuable

• 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

Previous experience in finance commodities or energy markets is helpful but not required. More important is a demonstrated ability to learn quickly conduct rigorous quantitative research and take ownership of challenging problems.