Quantitative Product Development Engineer

Overview

We are seeking a Quantitative Product Development Engineer to design, build, and enhance quantitative products across the full lifecycle, from research and prototyping through to production deployment and ongoing performance monitoring. You will work closely with quant researchers, engineering teams, risk, and stakeholders to translate quantitative methodologies into robust, scalable, and well-governed solutions.

Key Responsibilities

• Develop and productionise quantitative models and analytics, ensuring accuracy, robustness, and maintainability.
• Design data pipelines and feature engineering workflows for time-series and cross-sectional data.
• Implement model back-testing, simulation frameworks, and performance attribution with clear documentation.
• Build APIs, services, and libraries to expose quantitative capabilities to downstream users and systems.
• Apply software engineering best practices: code reviews, unit/integration testing, CI/CD, and version control.
• Monitor deployed models, manage drift, define alerts, and support incident resolution.
• Partner with risk and governance functions to meet model validation, auditability, and controls requirements.

Required Skills & Experience

• Degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative discipline (Master’s or PhD advantageous).
• Strong programming skills in C++ and Python; experience with performant implementations (e.g., NumPy, pandas, SciPy) and profiling/optimisation.
• Production engineering experience with cloud and/or containerisation (e.g., Docker, Kubernetes) and CI/CD pipelines.
• Solid understanding of statistics, linear algebra, optimisation, and numerical methods.
• Experience with data stores and query languages (SQL); familiarity with time-series databases and distributed processing is beneficial.
• Ability to write clean, well-tested, well-documented code and maintain technical documentation.
• Strong communication skills, with the ability to explain quantitative concepts to technical and non-technical stakeholders.

Desirable

• Experience with model governance, validation processes, and reproducible research practices.
• Knowledge of market data, trading, risk metrics, or financial instruments.
• Familiarity with message queues, event-driven architectures, and low-latency systems.

Working Style

You will thrive in a collaborative, delivery-focused environment, balancing research rigour with engineering discipline, and taking ownership from concept to reliable production operation.