The Automated Trading Strategies (ATS) group is responsible for systematic trading across FX, Rates, Commodities, and Credit markets. We design and implement cutting-edge proprietary quantitative models that drive our automated trading systems - including pricing, risk management and execution. This role offers career growth, exposure to a dynamic and collaborative environment, and the opportunity to drive revenue and expand our business.

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Job Summary

As an Analyst or Associate in the Global Commodities ATS group, you will focus on commodities markets, including precious and base metals, energy, agriculture and index products. You will work closely with the trading desk to identify revenue opportunities and enhance our automated trading strategies. You'll be part of a team that values collaboration, innovation, and continuous learning, contributing to the firm's success and your professional growth.

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Job Responsibilities

• Analyze large datasets to identify trading patterns and revenue opportunities
• Conduct research to develop and improve quantitative models for trading strategies
• Backtest and evaluate pricing, risk management, and execution algorithms
• Review trading performance and contribute to data-driven decision making
• Maintain and enhance trading software systems and analytical tools
• Support day-to-day trading operations

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Required qualifications, capabilities and skills

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, Engineering, or a related quantitative field
• Proficiency in programming with C++, Java, or another object-oriented language
• Solid understanding of statistics and data analysis techniques
• Attention to detail, adaptability, and a collaborative mindset
• Demonstrated interest in financial markets and systematic trading

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Preferred qualifications, capabilities and skills

• Prior work experience in commodities, quantitative trading, or a similar analytical role
• Familiarity with order types, L2 market data, and central limit order books
• Experience with KDB+/q or similar time-series databases