This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
As an Algorithmic Trading Quantitative Analyst (Vice President) within our Commodities team in London, you will be instrumental in designing, developing, and optimizing advanced electronic market-making and algorithmic pricing models. This is an exceptional opportunity to apply your quantitative expertise in a practical, production-oriented setting, directly impacting trading operations and risk management. You will work within a closely integrated team, gaining exposure to the microstructure of commodities markets and contributing to the expansion of systematic strategies in a leading financial institution. This role offers the chance to bridge sophisticated theoretical models with tangible, real-world trading outcomes, making a significant contribution to Citi's competitive edge in the global commodities market.
Job Responsibility:
Research, design, and implement electronic market-making models, including hedging algorithms, bid-offer models, price predictors, and automated pricing frameworks for Commodities
Analyze the performance of existing models in actual market-making operations and provide recommendations for continuous improvement and optimization
Develop and extend functionality of current production models and build robust hedging strategies across various Commodity markets
Collaborate closely with traders, quants, and technologists to ensure models generate value, are optimally integrated, and are aligned with business objectives
Focus on thorough model testing and the practical implementation of appropriate models, ensuring their effectiveness and reliability in a live trading environment
Understand the balance between model sophistication and ease of implementation, striving for pragmatic solutions that achieve goals efficiently
Requirements:
Proven experience working in systematic trading, encompassing areas such as execution algorithms, statistical arbitrage, or electronic market making
Demonstrated success in solving practical problems, ideally within finance, using advanced statistical and machine learning techniques
Strong programming proficiency in Java/C++ and Python is essential, coupled with the ability to work effectively with large datasets
Proficiency in database applications, such as SQL and KDB, for data manipulation and analysis
M.S. or Ph.D. in a highly quantitative field such as mathematics, physics, statistics, engineering, or financial engineering
Attention to detail, strong problem-solving capabilities, and the ability to articulate complex quantitative concepts in simple terms to diverse stakeholders