OTC Java Developer

Bonhill Partners; Greater London; Hybrid; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Hybrid work

Job summary

Bonhill Partners in London seeks a highly quantitative Senior Quant Developer to join the OTC Pricing team, bridging Quantitative Research and production engineering.

You will use Python for research and data modelling and Java to build high‑performance, distributed pricing systems that affect client pricing, flow analysis and hedging strategies for a global institutional liquidity provider.

Hybrid work arrangement: 3–4 days in the London office to enable high-bandwidth collaboration.

Qualifications

• Java expertise: 5+ years, OO design, concurrency, multi-region systems.
• Python proficiency with NumPy/SciPy/Pandas for data analysis and backtesting.
• Experience with numerical optimisation and ML for pricing problems.
• Direct market experience in client pricing or algorithmic trading.
• Strong numerical academic background (Math/Physics/Quantitative Finance).

Responsibilities

• Model Implementation (Java): architect and implement complex pricing/hedging models in a high-performance framework.
• Quantitative Research (Python): analyse large datasets, develop alpha signals, refine pricing logic.
• Distributed Systems: manage multi-region deployments ensuring 24/7 availability.
• Optimal Hedging: design automated hedging balancing market impact and liquidity.
• Client Analytics: model flow toxicity to optimise bespoke pricing tiers and spreads.

Skills

Java

Python

NumPy

SciPy

Pandas

KDB+/Q

AWS

Docker

Kubernetes

Low latency

Machine Learning

Tools

KDB+/Q

AWS

Docker

Kubernetes

Job description

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.