Quantitative Developer / Model Validator

Job title: Quantitative Developer / Model Validator

About the Role The Quantitative Developer / Model Validator will support the independent validation and review of algorithmic trading models, pricing and risk engines, and related quantitative tools across the firm. A core part of the role will involve reading, understanding, and challenging production-quality C++ implementation of quantitative trading models and related analytics libraries. The role is particularly relevant for systematic and algorithmic trading strategies, including statistical arbitrage and model-driven trading logic, and requires the ability to bridge quantitative analysis with implementation review, data analysis, and software-oriented validation work.

What You'll Do

• Perform independent validation of algorithmic trading models, pricing models, and risk engines, with particular focus on methodology, implementation, controls, and intended use.
• Review and challenge model implementation in C++, including ability to understand core logic, identify implementation weaknesses, and assess whether code behaviour is consistent with documented methodology.
• Analyse and validate systematic trading strategies, including statistical arbitrage, relative-value, and model-driven execution logic.
• Assess model assumptions, inputs, parameterisation, calibration, limitations, monitoring, and governance arrangements.
• Support validation testing using Python and other tools, including benchmark analysis, sensitivity analysis, scenario testing, and out-of-sample review where relevant.
• Work with datasets used in model development and validation, including data extraction, cleansing, integrity checks, and preparation of evidence for validation work.
• Contribute to validation of software-heavy quantitative tools and front-office analytics libraries, including review of implementation evidence, model changes, and production behaviour.
• Produce clear and high-quality validation documentation, including technical write-ups in LaTeX where appropriate.
• Support development of validation tooling, testing utilities, and data workflows that improve the efficiency and repeatability of model review.

What We're Looking For

• Essential:
• Strong programming capability in C++ and Python.
• Ability to read, understand, and challenge production-quality C++ code used in quantitative models and trading systems.
• Statistical arbitrage / systematic trading familiarity (factor models, mean-reversion signals, execution/TC modelling, drawdown/regime risk).
• Experience reviewing quantitative model implementation, testing logic, and identifying implementation weaknesses in software-heavy environments.
• Professional in creating well-structured documents using scientific typesetting software i.e. LaTeX etc.
• Ability to obtain data from multiple sources, link and analyse the information, perform data integrity checks.
• Master’s degree/PhD in Maths, Physics, Engineering, Quantitative Finance, Computer Science, Financial Economics, Econometrics or any related field (or equivalent qualification or experience).
• Desirable:
• Experience developing quantitative trading systems in C++.
• Familiarity with order-driven trading systems, market microstructure, and execution workflows.
• AI/ML exposure for trading/risk (e.g., tree-based models, regularisation, neural nets, time-series ML), including awareness of validation pitfalls (leakage, drift, reproducibility).
• Exposure to front-office quantitative libraries or production pricing / risk engines.
• Good grounding in stochastic processes, numerical methods, computational finance, and quantitative risk management.
• Software development mindset with interest in building tools, improving testing processes, and supporting model validation through technical infrastructure.

Nice to Have

• Experience developing quantitative trading systems in C++.
• Familiarity with order-driven trading systems, market microstructure, and execution workflows.
• AI/ML exposure for trading/risk (e.g., tree-based models, regularisation, neural nets, time-series ML), including awareness of validation pitfalls (leakage, drift, reproducibility).
• Exposure to front-office quantitative libraries or production pricing / risk engines.
• Good grounding in stochastic processes, numerical methods, computational finance, and quantitative risk management.
• Software development mindset with interest in building tools, improving testing processes, and supporting model validation through technical infrastructure.