Helix Junior Quantitative Developer

🔒 Confidential Employer
Posted 21 March 2026
LOCATION
London
TYPE
Full-time
LEVEL
Mid-Senior level
CATEGORY
Finance
This employer holds a UK Home Office sponsor license — sponsorship for this specific role is at the employer’s discretion

SKILLS

Python Numpy Unix systems Statistical methods Equity market microstructure Graph-based (DAG) data processing frameworks

FULL DESCRIPTION

[Employer hidden — view at passion-project.co.uk] is seeking a Junior Quantitative Developer to join their Helix Equities Statistical Arbitrage (StatArb) team in London. The role involves developing and supporting complex quantitative strategies, working with large-scale data processing, statistical modeling, and high-performance systems. The candidate will collaborate with researchers and technologists in a fast-paced, agile environment, contributing to research, development, and live trading platforms.

About the Role:

[Employer hidden] Helix is an Equities Statistical Arbitrage (StatArb) business line within [Employer hidden] Funds, which trades 24/6 globally. [Employer hidden] Helix has been live since 2021 and continues to evolve rapidly as part of an ambitious roadmap focused on expanding strategy breadth and capacity. Helix consists of four groups: Research, Quantitative Development, Technology and Data. The project is highly collaborative; the team are looking for a Quantitative Developer capable of working on all aspects of the trading platform - research, development, and live trading platforms. Technology is a front-line function within Helix, playing a critical role in systematic trading. Both the research platform and the production trading system are built on top of custom implementation of high-performance python graph (DAG). The same graph framework is being used across the whole environment – research and back testing, live trading, analytics, risk, position keeping and P&L.

Responsibilities:

  • Develop and support a complex quantitative StatArb strategies involving large- scale data processing, sophisticated statistical modelling, portfolio construction and highly optimised execution.
  • Build and maintain systems handling highly diversified equity portfolios.
  • Monitoring transaction costs and behaviour of a high turnover strategy.
  • Process and analyse vast historical datasets for research and back-testing, alongside real-time, tick-level market data for live trading.
  • Contribute to the design and use of a high-performance, graph-based (DAG) framework enabling concurrent data processing for research and production.
  • Monitor and manage execution quality, transaction costs and market risks arising from changing market regimes and small statistical effects.
  • Work closely with quantitative researchers to enhance tooling, frameworks and shared feature libraries.
  • Take ownership of system components in a fast-paced, agile environment, working both independently and collaboratively as required.
  • Participate in live trading support, including interaction with orders and brokers as part of a rota (FCA certification required).

Qualifications:

  • 3 years’ experience in a technical role within the finance industry (investment bank, hedge fund and associated firms)
  • Degree in Mathematics or Physics preferred; other STEM subjects such as Computer Science will also be considered.
  • Python: Strong software developer with in-depth knowledge and experience.
  • Numpy (including numba): in-depth knowledge is required.
  • Strong knowledge of Unix systems (processes, memory, I/O).
  • Deep understanding of statistical methods, numerical optimisation and equity market microstructure.
  • Experience working with graph-based (DAG) data processing frameworks.

Personal Attributes:

  • Highly analytical with a strong sense of ownership and accountability.
  • Comfortable tackling complex, ambiguous problems with limited oversight.
  • Collaborative mindset with the ability to work closely with researchers, technologists and trading operations.
  • Calm and reliable under pressure, particularly in live trading environments.

Key Outcomes:

  • Delivery of robust, scalable and high-performance systems supporting live trading.
  • Tangible improvements to research productivity, execution quality and platform stability.
  • Technical excellence and system ownership.
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