Quantitative Research Analyst

Employment type: Permanent Aspect Capital is an award-winning systematic hedge fund based in London. We manage over $9 billion of client assets. Research sits at the core of our investment process, playing a critical role in the success of our business. The Role: Our hypothesis-driven research spans a broad range of strategies, asset classes and markets across different regions, and supports multiple products across Trend Following, Absolute Return and Customised Solutions. We are looking for a Quantitative Researcher with strong technical foundations to join us. You will work in a dynamic, collegiate, multi-disciplinary research team on projects spanning model development, portfolio construction, risk management and market access. Key Responsibilities: Researching, developing and maintaining systematic investment models across a range of signals and asset classes Formulating and solving portfolio construction and optimisation problems Conducting rigorous statistical analysis of diverse input data for systematic investment strategies, testing the robustness of results and recording assumptions and caveats Presenting findings and their limitations accurately Your experience: A top-class undergraduate degree, and ideally an MSc or PhD, in a numerate discipline such as mathematics, statistics, physics, engineering, operations research or computer science Up to 3 years of relevant industry , we also welcome candidates joining directly from academia too A strong understanding of core concepts in probability, statistics, linear algebra and machine learning, with the ability to reason from first principles rather than relying on black-box tools The ability to analyse and synthesise information to solve problems, question assumptions, challenge results constructively (including your own), test fundamentals, spot anomalies, and recognise when a result is too weak to act on and flag it Strong programming ability in Python or MATLAB A background in optimisation is highly desirable, for example convex optimisation, linear and quadratic programming, or stochastic and numerical optimisation Hands-on experience applying machine learning methods such as gradient boosting, neural networks or regularised regression to noisy, non-stationary data, using scikit-learn, PyTorch or similar, is a plus, as is a solid grasp of overfitting, cross-validation and out-of-sample testing If this role sounds of interest, we would love to hear from you.