Commodities Quantitative Research Extern, Rice University

Jain Global; Houston (TX); On-site; USD 28,000 - 41,000; Part time

Job summary

Jain Global is seeking a Quantitative Research Extern to collaborate with our commodities investment teams. You will apply quantitative methods, fundamental analysis, and data-driven research to real-world trading problems, exploring datasets such as futures prices, weather data, and energy fundamentals.

You will work directly with portfolio managers and analysts, conducting independent research, developing hypotheses, and translating findings into reproducible code and actionable insights in a

Qualifications

• Currently enrolled in a bachelor's, master's, or PhD program in a quantitative discipline such as mathematics, statistics, computer science, engineering, physics, economics, or related field.
• Strong quantitative, analytical, and problem-solving skills.
• Solid foundation in probability, statistics, and statistical modeling.
• Experience with real-world datasets, noise, missing data, overfitting, and out-of-sample testing.
• Programming experience in Python; experience with C++ or R is valuable.
• Experience working with SQL, Python data tools, and/or Microsoft Excel.

Responsibilities

• Research, develop, and back test statistical arbitrage and relative-value strategies.
• Conduct fundamentally driven trade research in various commodities markets.
• Develop derivative pricing, volatility, and risk models.
• Research and implement machine learning approaches for forecasting and signal generation.
• Build time-series and predictive models for commodity prices and market fundamentals.
• Evaluate new datasets and engineer features for investment models.
• Develop analytics, visualization tools, and dashboards for research and trading.

Skills

Python

C++

R

SQL

Time-series

Statistics

Machine learning

Data analysis

Education

Bachelor's degree in quantitative discipline

Master's degree

PhD

Tools

SQL

Python data tools

Excel

Job description

Job Description 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.

Job Description 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.

• 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.

What You'll Do

• 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.

What We're Looking For

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 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; experience with C++, R, or similar languages is also valuable
• Demonstrated experience working with and analysing 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 modelling, 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.