FX Quantitative Developer

Bonhill Partners are working closely with a well-established Crypto exchange to hire a Quantitative Java Developer to work on their low latency/HFQ trading platforms.

This is a Hybrid role with a requirement of 2/3 days in office.

Requirements:

• Advanced Java development. Expert knowledge of Object-Oriented (OO) design, concurrency, and building high-performance, distributed multi-region systems.
• Python Proficiency: Expert use of the Python stack (NumPy, SciPy, Pandas) for quantitative data analysis, backtesting, and model prototyping.
• Numerical Optimisation & ML: Proven experience applying numerical optimisation techniques (e.g., convex optimisation, gradient descent) and Machine Learning models to solve real-world pricing or trading problems.
• Market Experience: Direct experience in client pricing or equivalent algorithmic trading roles within liquid markets (e.g., FX, ETFs, Equities, or Crypto).
• Quantitative Foundation: Strong academic background in a numerical field (Mathematics, Physics, or Quantitative Finance).

Preferred Qualifications:

• Infrastructure: Experience with cloud-native deployments (AWS), Docker, and Kubernetes.
• Low-Latency: Familiarity with performance tuning (GC optimisation, LMAX Disruptor) is a plus but secondary to distributed systems expertise.
• Derivatives Knowledge: Understanding of derivatives pricing and risk management across Futures, Forwards, NDFs, and CFDs.

The salary available is up to £160k + X2 yearly bonus + benefits.

• Advanced Java development. Expert knowledge of Object-Oriented (OO) design, concurrency, and building high-performance, distributed multi-region systems.
• Python Proficiency: Expert use of the Python stack (NumPy, SciPy, Pandas) for quantitative data analysis, backtesting, and model prototyping.
• Numerical Optimisation & ML: Proven experience applying numerical optimisation techniques (e.g., convex optimisation, gradient descent) and Machine Learning models to solve real-world pricing or trading problems.
• Market Experience: Direct experience in client pricing or equivalent algorithmic trading roles within liquid markets (e.g., FX, ETFs, Equities, or Crypto).
• Quantitative Foundation: Strong academic background in a numerical field (Mathematics, Physics, or Quantitative Finance)., * Infrastructure: Experience with cloud-native deployments (AWS), Docker, and Kubernetes.
• Low-Latency: Familiarity with performance tuning (GC optimisation, LMAX Disruptor) is a plus but secondary to distributed systems expertise.
• Derivatives Knowledge: Understanding of derivatives pricing and risk management across Futures, Forwards, NDFs, and CFDs.