Quantitative Trader in London Area

Job description

Summary

A quantitative trader with several years of experience in a small, high-ownership pod structure, covering the full lifecycle of research, modelling, implementation, and daily trading decision-making. Strong record of generating new alpha ideas, improving model efficiency, and contributing to systematic trading strategies across global equities and related products.

Core Strengths

1. Full-Stack Quant Experience

• End-to-end exposure across research, quant development, and portfolio/trading decisions.
• Comfortable with tight execution loops and taking ownership of full model pipelines.
• Experienced in debugging production strategies and improving robustness.

2. Alpha & Feature Innovation

• Regular contributor of new features, signals, and ML-driven model improvements.
• Skilled at evaluating new data sources and optimising existing input pipelines.
• Experience with feature engineering, cross-validation techniques, and model diagnostics.

3. Market Awareness & Risk Sensitivity

• Background in systematic long/short equities across US and Europe.
• Additional exposure to fixed income and market-making style risk management.
• Strong intuition for differentiating model-driven losses vs. Risk-management errors.

4. Technical Skills

• Strong programming background (Python, C++/C#, or similar).
• Experience with production-grade ML workflows.
• Familiar with distributed compute, model optimisation, and low-latency considerations.

5. Small-Team Versatility

• Works in a 4-person pod—responsible for everything from research to deployment.
• Able to operate independently with minimal structure.
• Thrives in environments where decisions are fast, data-driven, and collaborative.

Trading & Research Focus

• Strategies: Systematic L/S equities, with some exposure to fixed-income signals and hedging activities.
• Style: Medium- to high-frequency stat-arb ideas (non-HFT).
• Daily Activities:
• Monitoring model outputs
• Intraday adjustments to risk
• Evaluating PnL drivers
• Running daily research iterations
• Implementing improvements to execution logic

Practical Achievements

• Delivered multiple incremental improvements to alpha and risk models.
• Designed or co-designed new ML-based components that fed directly into PnL improvements.
• Improved data pipelines and feature computation speed, increasing research efficiency.
• Helped reduce risk-related drawdowns by identifying and correcting model sensitivities.

Motivations

• Seeking a more structured, high-performance market-making environment like Optiver.
• Wants to work with stronger PMs/traders and avoid bottlenecks introduced by inconsistent risk management.

Extra information

Status

Open

Education Level

Secondary School

Location

London Area

Type of Contract

Casual / Part Time Jobs

Published at

15-12-2025

Profession type

Accountancy

Full UK/EU driving license preferred

No

Car Preferred

No

Must be eligible to work in the EU

No

Cover Letter Required

No

Languages

English