3 days a week in the office - based in London
We are seeking a highly quantitative Senior Quant Developer (QD) to join our OTC Pricing team. This role sits at the critical intersection of Quantitative Research (QR) and production engineering. You will be a "proper" QD- collaborating directly on the mathematical design and owning the production implementation of client pricing and liquidity models.
You will operate across a dual-language stack: utilising Python for research and data-driven modelling, and Java for architecting high-performance, distributed pricing systems. Your work will directly impact client pricing optimisation, flow analysis, and optimal hedging strategies for a global institutional liquidity provider.
Duties and Responsibilities:
- Model Implementation (Java): Architect and implement complex quantitative models (pricing, hedging, and optimisation) within our mission-critical, high-performance Java framework.
- Quantitative Research (Python): Partner with QRs to analyse large-scale datasets, develop alpha signals, and refine pricing skews and spread optimisation logic.
- Distributed Systems: Manage the challenges of deploying pricing logic across a multi-region architecture , ensuring consistency and high availability for 24/7 global trading.
- Optimal Hedging: Design and implement automated hedging algorithms that balance market impact, execution risk, and liquidity constraints.
- Client Analytics: Model toxicity and decay in client flow to optimise bespoke pricing tiers and maximise spread capture. Required Skills and experience:
- Java Expertise: 5+ years of 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:
- KDB+/Q: Experience with KDB+/Q is a significant advantage. We are willing to train candidates with strong backgrounds in functional programming.
- 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.
What we offer:
- Hybrid Work: A modern office environment in London with a 3-4 day in-office expectation to foster high-bandwidth collaboration.
- Impact: A role where QDs are primary contributors to the research and deployment lifecycle.
- Compensation: Competitive salary with two discretionary bonus awards per year.