The Automated Trading Strategies (ATS) group is responsible for systematic trading across FX, Rates, Commodities, and Credit markets. The team is responsible for a broad scope including the design and implementing of cutting edge proprietary quantitative models that drive our automated trading systems (pricing, risk management and execution), the oversight of day-to-day risk and operations, and the optimization Franchise client liquidity offering in a data-driven manner.
As an Associate in Automated Trading Strategies, you will be primarily focusing on the US Interest Rate Swap market. You must be responsible, independent, driven, and able to work in smooth collaboration with the wider team. The environment is fast-paced and challenging. The group is globally distributed so clear written and verbal communication is required. Members of the team are also expected to cover a wide range of responsibilities - spanning trading, quantitative research, and technology—and some on call time will be expected.
Job Responsibilities
- Produce innovative research on quantitative trading strategies
- Analyze of data to identify patterns and revenue opportunities
- Conduct back testing and assessing pricing, risk management and execution strategies
- Expand the group’s library of modelling, analytics, and automation tools
- Review trading performance and making data driven decisions
- Resolve day-to-day trading issues
Required qualifications, capabilities, and skills
- Degree in computer science, math, physics, engineering, or other quantitative fields
- Relevant full-time experience
- Strong programming skills in C++, Java, or other object-oriented languages
- Knowledge of probability, statistics, and experience with advanced data analysis techniques
- Attention to detail, adaptable, driven and collaborative
- Active interest in markets and quantitative trading
Preferred qualifications, capabilities, and skills
- Prior experience in Rates markets (cash or swaps)
- Familiarity with automated trading systems
- Experience with large-scale data sets and optimizing low-latency systems