Quantitative AI Strategist - Trading Team
Quantitative Research · London · Full-time · On-site
Annual base salary: £180,000 – £220,000 GBP
Apply machine learning techniques directly to trading strategy design and portfolio construction.
Role description
Westren Capital is a London-based proprietary trading firm specialising in digital asset markets. This role sits at the intersection of our quantitative research and AI functions, applying machine learning techniques directly to trading strategy design rather than as a separate research exercise.
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. That requires intellectual honesty, a tolerance for ambiguity, and a willingness to discard ideas that do not survive contact with reality.
Responsibilities
Apply machine learning techniques to trading strategy design, from signal generation through to portfolio construction
Evaluate where ML genuinely adds value over simpler statistical approaches, and where it's just complexity for its own sake
Work with large, messy market and alternative datasets, building pipelines resistant to lookahead and leakage
Collaborate with traders and engineers to move strategies from research into live production without losing what made them work
Monitor live strategies for performance drift and diagnose whether it reflects genuine regime change or a modelling flaw
Stay current with ML research relevant to markets, filtering aggressively for what's actually applicable versus what's merely interesting
Requirements
Requirements:
Strong grounding in both statistical modelling and machine learning, with the judgement to know when to use which
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++ is a plus
Demonstrated experience applying ML to a real, measurable problem, not just a benchmark dataset
Comfortable working in a collaborative, high-performance environment where feedback is direct and iteration cycles are fast
Healthy scepticism toward your own models; the willingness to kill an idea that doesn't survive contact with live markets