Undergraduate degree is required; Advanced degree in a quantitative discipline (computer science, applied mathematics, economics, quantitative finance, engineering) or equivalent practitioner experience is preferred
2+ years’ experience in commodity markets developing market research, trading, or hedging/risk management strategies such as netback/deal economics, signal backtesting, stress testing, and/or hedge effectiveness is preferred but not required
2+ years’ experience in physical/cash markets of metals, power/electricity markets, natural gas, crude oil, refined products, and/or ocean freight is preferred but not required
2+ years’ experience applying time series analysis of mean-reverting and cointegrated commodity prices/premiums (e.g., ARIMA, VECM, GARCH),supervised learning (e.g., SVM, LSTM, parallel or sequential tree-based models), unsupervised learning (e.g., PCA, K-means clustering), optimization (e.g., LP/MILP and stochastic models), and/or NLP sentiment analysis is preferred but not required
6+ months’ experience with LLM-based analytics in enterprise settings (e.g., document/comms extraction, RAG, evaluation/guardrails) is preferred but not required
Experience integrating data science and/or automation workflows with CTRM/ETRM systems via API is preferred but not required
Experience automating middle/back-office processes with PRA and LLMs (e.g., pre-trade KYC/credit, trade execution, compliance, logistics, trade settlement) is preferred but not required
Production-quality Python for model development, including optimization, time series forecasting, feature engineering, backtesting, and evaluation using standard DS toolkits (e.g., pandas, scikit-learn, statsmodels, gradient boosting, etc.) is required
Familiarity with pytest and reproducible research workflow; experience operationalizing models: versioned experiments, CI/CD, model monitoring and drift detection, and collaboration with engineering on APIs and model/solution design is required
Experience applying advanced analytical and statistical methods to solve business problems involving commodity markets is required
Ability to explain commodity market nuances and/or complex analytics approaches and results in layman’s terms to executive audiences across commodity trading, operations, supply chain / logistics, and finance / accounting functions is required
