Quant Developer – Systematic Trading & ML Tools

Paragon Alpha - Hedge Fund Talent Business

Greater London

On-site

GBP 90,000 - 150,000

Full time

3 days ago
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Job summary

Paragon Alpha – Hedge Fund Talent Business is seeking an experienced Quantitative Developer to join a team working directly with Quant Researchers and Portfolio Managers. Embedded role bridging research, systematic trading and software engineering.

You’ll help build and evolve the research-to-production framework, improve workflows, and raise engineering standards. There will be exposure to ML-driven research and infrastructure to support development, testing and deployment of systematic

Qualifications

  • 5+ years' experience as a Quantitative Developer, Quant Technologist or similar.
  • Experience working directly with Quantitative Researchers within systematic trading.
  • Good understanding of quantitative strategies and the underlying investment logic.
  • Experience taking research code and strategies into robust production environments.
  • Exposure to ML pipelines and production machine learning.
  • Strong software engineering practices across testing, architecture and code quality.

Responsibilities

  • Build and evolve the firm's quantitative trading and research framework, working closely with researchers to take strategies from research through to production.
  • Improve research workflows, productionise trading strategies, building tools and infrastructure across the strategy lifecycle.
  • Raise engineering standards across the research environment.
  • Contribute to projects involving machine learning and AI-driven research infrastructure.

Skills

Quantitative Development
Research collaboration
Quantitative strategies
ML pipelines
Software engineering

Job description

Paragon Alpha – Hedge Fund Talent Business is seeking an experienced Quantitative Developer to join a team working directly with Quant Researchers and Portfolio Managers. Embedded role bridging research, systematic trading and software engineering.

You’ll help build and evolve the research-to-production framework, improve workflows, and raise engineering standards. There will be exposure to ML-driven research and infrastructure to support development, testing and deployment of systematic

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