Quantitative Developer

Location: London, Liverpool OR Glasgow - Hybrid

The Opportunity

We looking for an experienced Quantitative Developer to help develop and implement portfolio risk and performance analytics within a modern investment technology environment.

This is an opportunity to combine quantitative finance knowledge with strong software engineering skills, turning complex financial models into robust, reusable and production-quality applications.

You'll work closely with platform engineers and investment stakeholders to build and validate calculation models that support portfolio analysis, risk measurement and investment decision-making.

The focus is on practical implementation and delivery rather than purely academic research.

Key Responsibilities

• Develop and implement portfolio risk and performance models using Python.
• Translate quantitative methodologies into reliable, tested and reusable software.
• Build calculations covering portfolio returns, volatility, covariance and risk contribution.
• Implement and validate equity and multi-asset portfolio risk models.
• Develop and maintain factor models and tracking error calculations.
• Support benchmark comparisons and portfolio performance analysis.
• Handle missing historical data, proxy data and other data-quality challenges.
• Optimise numerical calculations and improve application performance.
• Develop numerical tests to validate model accuracy and consistency.
• Maintain high standards of code quality, version control and peer review.
• Work closely with platform engineers to integrate models into production calculation services.

Technical Skills & Experience

• Experience with NumPy, pandas and other numerical or scientific Python libraries.
• Strong understanding of quantitative modelling and numerical calculations.
• Experience working with SQL and structured financial datasets.
• Experience implementing and testing quantitative models in production environments.
• Knowledge of numerical testing, performance optimisation and code validation.
• Good software engineering practices, including Git, version control and code reviews.

Experience in some of the following areas is important:

• Equity and multi-asset portfolio risk.
• Portfolio returns and volatility.
• Covariance and correlation analysis.
• Historical data analysis and proxy treatment.

Additional experience with fixed-income risk and performance attribution would be beneficial.

What We're Looking For

We're looking for someone who understands both the mathematics behind portfolio risk and the engineering required to turn those calculations into reliable software.

You'll be comfortable working with complex financial data, validating quantitative outputs and collaborating with engineers to deliver production-ready solutions.

You don't need to be an expert across every asset class. Strong equity-risk knowledge, Python development and the ability to implement and test quantitative models are the priorities.

Experience using AI coding assistants is expected, alongside disciplined code review, testing and validation of generated code.

You'll be joining a small, collaborative engineering team where you'll have the opportunity to take ownership of important investment analytics capabilities.