Quantitative Research Analyst

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

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