Quantitative Developer

We are seeking an exceptional Quantitative Developer to play a pivotal role in the development of proprietary trading models and alpha-generating signals. Reporting directly to a senior Portfolio Manager, the successful candidate will contribute meaningfully to both the refinement of existing signal infrastructure and the expansion into new strategies and asset classes. This is a high-impact position for a developer who thrives at the intersection of rigorous quantitative analysis and practical implementation. You will be responsible for hypothesis-driven research, translating ideas into robust, production-ready code, and working collaboratively within a performance-oriented investment team. Responsibilities Design and develop quantitative trading models and systematic alpha signals across a range of asset classes and market regimes Conduct rigorous, hypothesis-driven research from ideation through to back testing, validation, and live deployment Collaborate closely with the Portfolio

Manager to evaluate signal quality, portfolio fit, and capacity constraints Continuously monitor and refine existing signals, identifying areas for improvement in both breadth and predictive power Requirements Master's or PhD in a quantitative discipline (Mathematics, Statistics, Physics, Computer Science, or similar) Demonstrable research experience in an academic or industry setting, with a focus on computation, numerical analysis, pattern recognition, or related disciplines Strong command of classical and Bayesian statistics, optimisation, numerical methods, and data science Proven ability to translate research hypotheses into well-structured, efficient coded Exceptional analytical and problem-solving capabilities, with the ability to draw meaningful insight from complex, high-dimensional datasets A background in finance or economics is not required; intellectual curiosity and research rigour are what matter 3 years in Proficiency in one or more of: Python, C/C++, Java, Solidity, or SQL Experience working with alternative datasets is advantageous - including consumer data (e.g. Experian, Dun & Bradstreet), commodities or weather-related datasets, NLP/news sentiment, or satellite imager