CIB QR - Quantitative Research AAO (Analytics, Automation & Optimization), Systematic Trading, Vice President
The role of the QR Systematic Trading group is to identify opportunities to transform, automate and optimize our trading operations and to define and implement cutting-edge next generation analytics to support this business transformation. We cover all equities businesses and work closely with traders to develop data-driven solutions such as algorithmic strategies (high to low frequency), trading signals, risk models, portfolio optimization, flow categorization and clustering - and to combine them into automated trading processes or trading algorithms.
We are looking for a strong and experienced candidate to support primarily the APAC Delta 1 and Internal Market Making desks. Communication skills and drive are critical for this role as we expect the candidate to actively engage with the business and act as a culture carrier for modern data-driven methods and business automation.
- Build trading analytics and algorithmic trading strategies such as portfolio optimization, index arbitrage, statistical arbitrage and market making strategies (on Equity cash, Futures, ETFs) for the Delta One and Cash trading desks.
- Identify business opportunities and contribute to the entire lifecycle from idea generation to production: perform research, design prototype, implement analytics and strategies, monitor daily usage and analyze performance
- Support trading activity by investigating model and algorithm behavior (scenarios and post trade analysis, historical behavior)
- Devise hedging and trading strategies and build execution logic
- PhD or Master's Degree in a quantitative discipline from a top-tier institution
- At least five years of relevant quant experience in systematic quantitative trading in Equity or related asset classes
- Strong written and verbal communication skills, ability to convey the ideas behind complex research in a clear and precise manner
- A thorough understanding of algorithmic trading, market making, index and statistical arbitrage
- Good expertise in statistical modelling & optimization, including standard models, linear, convex & conic optimization)
- A strong coding background with proficiency in C++, Python and relevant quantitative packages (numpy, pandas). Ability to manipulate and analyze complex, large scale, high-dimensionality data from multiple sources. Knowledge of KDB/Q expected.
- Knowledge of derivatives pricing and risk management theory (vanilla options and volatility products) is a plus