At a Glance
- Tasks: Research and develop systematic investment models across diverse asset classes.
- Company: Award-winning hedge fund in London managing over $9 billion in assets.
- Benefits: Competitive salary, dynamic team environment, and opportunities for professional growth.
- Other info: Ideal for recent graduates or those with up to 3 years of industry experience.
- Why this job: Join a collaborative team and make impactful contributions to innovative investment strategies.
- Qualifications: Strong technical background in mathematics, statistics, or computer science; programming skills in Python or MATLAB.
The predicted salary is between 54000 - 66000 £ per year.
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 experience; we also welcome candidates joining directly from academia
- 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.
Quantitative Research Analyst in London employer: Aspect Capital
Aspect Capital is an exceptional employer located in the heart of London, offering a dynamic work culture that fosters innovation and collaboration. As a Senior Data Engineer, you will have access to cutting-edge technology and the opportunity for professional growth while working alongside talented quants and portfolio managers. The company prioritises employee development and provides a supportive environment where your contributions directly impact the success of the team and the organisation.
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We think you need these skills to ace Quantitative Research Analyst in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
