Quantitative Researcher – Machine Learning & High-Frequency Trading
A recruiter advertises machine-learning research in high- and mid-frequency delta-one trading. The role develops deep-learning predictors and moves validated signals into production.
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.
- Research features and predictive models using large market datasets.
- Optimise targets, sampling and cost assumptions in backtests.
- Train deep models at scale and work with engineers on deployment.
What they ask for
Required
- Deep learning and statistical reasoning
- Production quantitative models
- Python or another relevant language
- Large-scale data and distributed computing
- Technical communication
- Education
- Graduate or postgraduate STEM education, ideally focused on machine learning or statistics; precise minimum level not specified.
- Experience
- At least 3 years as a quant modeller or researcher, particularly in high- or mid-frequency trading.
- Languages and tools
- Markets
- equities
How to prepare
- Test a deep-learning signal with leakage-resistant chronological splits.
- Compare target definitions and transaction-cost assumptions.
- Benchmark distributed training and monitor out-of-sample decay.
What to study
- Financial Machine Learning
- Quantitative Research, Data and Backtesting
- Market Microstructure and Execution
The HFT Quantitative Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Westbury Partners’s site before applying: requirements, location and pay can change after we check them.
View the posting on Westbury Partners’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Westbury Partners and doesn’t handle applications.