The client, a proprietary trading firm, is looking to solve problems in financial modeling, algorithm development, and system optimization by applying advanced quantitative methods to their automated trading systems for global derivatives markets. They aim to stay ahead in competitive and fast-moving markets by iterating on models and systems.
Requirements
- Ability to write and maintain clean, organized, object-oriented Python code; C++ experience is a plus.
- Proven experience in at least one of the following: options and volatility modeling, statistical analysis on large data sets, or diagnosing and optimizing algorithm logic in latency-sensitive or high-throughput distributed systems.
- Expertise working with MySQL or similar databases for historical research and back-testing.
Responsibilities
- Apply advanced quantitative methods to solve problems in financial modeling, algorithm development, and system optimization.
- Collaborate with traders and developers to improve a complex, evolving algorithmic trading infrastructure.
- Design and implement tools for traders, explore new trading ideas, and refine existing strategies.
- Build, test, and maintain automated trading algorithms and risk management logic from scratch.
- Analyze and optimize system performance, including latency, stability, and execution quality.
- Conduct historical research and back-tests using large data sets and database tools.
- Communicate market developments, model behavior, and research insights to traders and other stakeholders.
Other
- 2+ years of full-time, professional experience in a quantitative research, trading, or high-performance technology role, ideally at an algorithmic trading firm or similar environment.
- Degree in mathematics, physics, engineering, computer science, or a closely related quantitative field.
- Passion for innovation and solving open-ended, data-rich problems.
- Strong written and verbal communication skills, with the ability to explain complex technical ideas clearly.
- Collaborative attitude with a competitive drive and comfort with metrics-based performance evaluation.
