Quant jobs / Millennium

Quantitative Researcher – Systematic Equities

A research role in a systematic equity team in London, working alongside a senior portfolio manager on new signals and strategies. It involves a large shared Python codebase for machine learning research.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Develop and backtest equity alpha signals from market, fundamental and alternative data
Fit statistical or machine learning models and deploy them into the investment process
Extend Python libraries used for ML research
Study intraday flow and event-driven effects in cash equities
Review code in a jointly owned research repo

What they ask for

Required

Python research and software skills
Alpha research in cash equities
Machine learning or statistical learning
Backtesting
Intraday equity trading knowledge
Clear communication

Preferred

Statistical arbitrage experience
Machine learning alphas
Event and alternative data
Economic intuition

Education: Master's or PhD in computer science, applied maths, statistics or similar, preferably from a top-ranked university
Experience: 3+ years in cash equity alpha research, flow research and intraday trading
Languages and tools: Python
Markets: equities

How to prepare

Replicate an equity signal paper and test it out of sample with transaction costs
Build a small ML pipeline with proper time-series cross-validation
Study statistical arbitrage: pairs, residual returns and factor neutralisation
Write modular, tested Python for a shared research library

What to study

The Quantitative Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

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View the posting on Millennium’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Millennium and doesn’t handle applications.