Python Quant Developer – Systematic Equities & Credit - McGregor Boyall

Python Quant Developer – Systematic Equities & Credit

I'm working with a leading global investment manager in London looking to hire a Python Quant Developer to support its systematic equities and credit strategies.

The position sits within a front-office-aligned engineering function, working closely with quantitative researchers and traders to develop the software and data infrastructure used throughout the research and trading process.

The focus is on building well-engineered Python applications, improving existing systems and taking responsibility for production technology.

The Role

You'll be joining a small, cross-functional engineering team responsible for developing and maintaining the technology supporting systematic trading strategies across equities and credit.

• Develop and maintain production Python applications, research tooling and financial data pipelines used by quantitative researchers and traders.
• Work closely with investment teams to translate research requirements into reliable, scalable software.
• Take ownership of applications throughout their lifecycle, including development, testing, deployment and ongoing improvements.
• Address engineering challenges around system performance, data quality and reliability, while contributing to good development practices.

What They're Looking For

• Strong Python development experience, ideally within an investment bank, hedge fund, asset manager or similar financial markets environment.
• Experience building production-quality software, with a good understanding of software engineering principles, testing and maintainable code.
• Familiarity with Python's scientific computing libraries, particularly NumPy, Pandas and SciPy.
• An understanding of equities markets, financial instruments and associated data. Credit experience would be advantageous but isn't essential.

Why It's Worth Considering

What's particularly interesting about this position is the proximity to systematic trading and the breadth of engineering work involved.

There is also scope to gain exposure to other strategies and areas of quantitative development over time.

This could be particularly interesting for a Python Developer or Quant Developer working within equities technology, pricing, research infrastructure or front-office systems who wants to move closer to systematic trading.

If this sounds relevant to your background, feel free to apply or get in touch for a confidential discussion.