Machine Learning Quant Engineer - Investment Banking

Harvey Nash
City of London, England
12 months ago
Applications closed

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Overview

Senior Quant Machine Learning Engineer sought by leading investment bank based in the city of London.

Inside IR35, 4 days a week on site

The role:

To lead the design and deployment of ML-driven models across our trading and investment platforms. This is a high-impact, front-office role offering direct collaboration with traders, quant researchers, and technologists at the forefront of financial innovation.

Your Role
  • Design, build, and deploy state-of-the-art ML models for alpha generation, portfolio construction, pricing, and risk management
  • Lead ML research initiatives and contribute to long-term modeling strategy across asset classes
  • Architect robust data pipelines and scalable model infrastructure for production deployment
  • Mentor junior quants and engineers; contribute to knowledge-sharing and model governance processes
  • Stay current with cutting-edge ML research (e.g., deep learning, generative models, reinforcement learning) and assess applicability to financial markets
  • Collaborate closely with cross-functional teams, including traders, data engineers, and software developers
What We\'re Looking For

Required:

  • 7+ years of experience in a quant/ML engineering or research role within a financial institution, hedge fund, or tech firm
  • Advanced degree (PhD or Master\'s) in Computer Science, Mathematics, Physics, Engineering, or related discipline
  • Strong expertise in modern ML techniques: time-series forecasting, deep learning, ensemble methods, NLP, or RL
  • Expert-level programming skills in Python and strong understanding of software engineering best practices
  • Experience deploying ML models to production in real-time or high-frequency environments
  • Deep understanding of financial markets and quantitative modeling

Preferred:

  • Experience in front-office roles or collaboration with trading desks
  • Familiarity with financial instruments across asset classes (equities, FX, fixed income, derivatives)
  • Experience with distributed computing frameworks (e.g., Spark, Dask) and cloud-native ML pipelines
  • Exposure to LLMs, graph learning, or other advanced AI methods
  • Strong publication record or open-source contributions in ML or quantitative finance

Please apply within for further details or call on (phone number removed)

Alex Reeder

Harvey Nash Finance & Banking


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