Quantitative Researcher - Trading Team

Westren Capital Ltd.; Greater London; Hybrid; GBP 60,000 - 180,000; Full time

Job summary

Westren Capital, a London-based proprietary trading firm, seeks researchers who can transform messy data into robust trading signals. You will work at the intersection of statistics, machine learning, and market microstructure, building strategies that operate in live markets.

The team is small and highly collaborative, valuing rigorous applied research over backtests. Candidates with strong Python and Linux experience, and a track record in quantitative research, will fit well in this

Qualifications

• Strong problem-solving ability and independent thinking; comfort with uncertain, non-obvious problems
• Experience developing quantitative trading strategies valued; evidence of rigorous applied research matters
• Solid grounding in statistical modelling and forecasting; familiarity with regression, tree-based models, or neural networks

Responsibilities

• Develop and test alpha signals across liquid markets focusing on robustness
• Work with large, messy datasets and turn them into usable insights
• Build fast, reproducible research pipelines resisting leakage and overfitting
• Collaborate with engineers to move ideas into production
• Study market microstructure and execution behaviour to improve returns
• Monitor live strategies and investigate performance drift

Skills

Problem solving

Independent thinking

Python programming

C++ experience

Linux experience

Machine learning

Statistical modelling

Forecasting techniques

Quantitative trading

Team collaboration

Education

Bachelor’s/Master’s/PhD in Mathematics/Statistics/Physics/CS

Tools

Python

C++

Linux

Git

Job description

Westren Capital is a London-based proprietary trading firm specialising in digital asset markets. We develop systematic trading strategies grounded in market microstructure across global venues. We hire from Mathematics, Physics, Computer Science and related fields, not for credentials, but for the ability to reason clearly under uncertainty and build things that work in production.

Our work sits at the intersection of statistical modelling, market microstructure, and engineering. The aim is straightforward, if not easy: extract durable signals from noisy data and convert them into scalable, risk-aware strategies. That requires intellectual honesty, a tolerance for ambiguity, and a willingness to discard ideas that do not survive contact with reality.

We operate in small, highly collaborative teams where research, engineering, and trading are tightly coupled. Outcomes matter. Not narratives, not backtests that look good in isolation, but strategies that hold up live. Compensation and progression reflect that directly.

Beyond trading, we invest in building internal tools, data infrastructure, and research workflows that compound over time. The edge is rarely a single idea; it is the system that produces and evolves ideas faster than others.

Responsibilities

• Develop and test alpha signals across liquid markets, with a focus on robustness rather than backtest aesthetics


• Work with large, messy datasets and turn them into something usable without fooling yourself in the process


• Build research pipelines that are fast, reproducible, and resistant to the usual traps (lookahead, leakage, overfitting)


• Collaborate with engineers to move ideas into production without breaking them in the process


• Study market microstructure and execution behaviour, not as theory but as something that directly impacts returns


• Monitor live strategies and investigate performance drift, which happens more often than people admit

Requirements

• Strong problem-solving ability and independent thinking; comfort working in areas where the answer is not obvious and often wrong the first few times


• Experience developing quantitative trading strategies is valued, but not required; evidence of rigorous, applied research matters more


• Solid grounding in statistical modelling and forecasting techniques; familiarity with machine learning methods such as regression, tree-based models, or neural networks


• Degree (Bachelor’s, Master’s, or PhD) in Mathematics, Statistics, Physics, Computer Science, or a related field


• Strong programming skills in Python; experience with C++ and working in a Linux environment is a plus


• Ability to work in a collaborative, high-performance environment where feedback is direct and iteration cycles are fast