Our client is one of the world’s premier investment firms. The firm deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of their effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.
Role:
Quantitative developer who will join the newly formed Central Research Technology team, which builds strategic solutions for research and live trading of quantitative strategies across multiple frequencies and products. This is a unique opportunity to grow your career with the team while building the next generation of research and quant trading systems.
Role/Responsibilities:
 Building a robust, scalable research infrastructure, including alpha estimation and risk
modeling components
 Developing a seamless but modular platform to handle all aspects of quant trading –
model building, optimization, and trade execution
 Building high-performance/low-latency modular systems for live trading and simulation
 Developing robust data checking, transformation and storage procedures
 Building visualization tools and monitors for market/trade/position/risk
 Maintenance and troubleshooting of trading systems
Requirements:
- 1-5 years of professional software engineering experience in a collaborative environment
- Bachelor’s degree or higher in computer science or other quantitative discipline
- Understanding of object oriented programming, design patterns and data structures
- Experience with the software delivery lifecycle and writing production-quality code
- Fluency in Python (and its ecosystem) for data analytics and research
- Familiarity with C
- Experience working with SQL and historical time series data
- Experience working with real time data/systems preferred
- Reasonable quantitative and statistical skills
- Experience in Finance is preferred
- Open source contributions/projects are a plus
- Team player with strong pride of ownership
- Detail oriented and quick learner in a fast-paced environment