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- Tasks: Design and optimise portfolio frameworks using machine learning for systematic equity strategies.
- Company: Leading multi-strategy hedge fund in London with a focus on innovation.
- Benefits: Competitive pay, performance bonuses, and opportunities for career growth.
- Why this job: Make a real impact in a highly technical, research-driven environment.
- Qualifications: 3-7 years in quantitative research with strong Python skills and a solid maths background.
- Other info: Collaborate closely with senior PMs and researchers for hands-on experience.
The predicted salary is between 43200 - 72000 £ per year.
Location: London
Fund Type: Multi-Strategy Hedge Fund
Team: Systematic Equities / Central Quant
Role Overview
We are looking to hire a Systematic Equity Portfolio Construction Quantitative Researcher to join our systematic equities platform. The role focuses on designing and improving portfolio construction, optimisation, and risk management frameworks for alpha signals generated by machine learning and quantitative research teams. You will work closely with systematic PMs and researchers to translate predictive signals into robust, scalable portfolios, optimising risk-adjusted returns while accounting for turnover, transaction costs, liquidity, and capacity constraints.
Key Responsibilities
- Design and maintain portfolio construction and optimisation frameworks for systematic equity strategies.
- Translate machine-learning-based alpha signals into investable portfolios with appropriate sizing and risk controls.
- Research and implement risk-aware optimisation techniques (mean-variance, risk parity, CVaR, drawdown-aware and robust optimisation).
- Build cross-sectional and time-series risk models, including factor exposure control and correlation management.
- Develop turnover, transaction cost, and liquidity-aware portfolio construction methods.
- Perform stress testing, scenario analysis, and regime-based risk analysis.
- Partner with PMs to refine signal weighting, portfolio constraints, and rebalancing logic.
- Productionise research in collaboration with engineering and trading teams.
Required Qualifications
- 3–7 years of experience in systematic equity research, portfolio construction, or quantitative risk within a hedge fund, asset manager, or proprietary trading firm.
- Strong academic background in Mathematics, Statistics, Computer Science, Engineering, or a related quantitative discipline.
- Advanced Python skills (NumPy, pandas, SciPy, optimisation libraries); Python is a must.
- Solid understanding of portfolio theory, optimisation, and equity market microstructure.
- Ability to communicate quantitative concepts clearly to PMs.
Preferred Experience
- Experience applying machine learning to portfolio construction, signal blending, or regime detection.
- Familiarity with transaction cost modelling (TCM) and capacity analysis.
- Exposure to multi-PM / pod-based platforms.
- Experience with large-scale data pipelines and research infrastructure.
- Knowledge of alternative risk measures and tail-risk modelling.
What We Offer
- Direct ownership of portfolio construction and risk frameworks for systematic equity strategies.
- Close collaboration with senior systematic PMs and researchers.
- Competitive compensation with strong performance-based upside.
- A highly technical, research-driven environment with real impact.
- Long-term career progression within a leading multi-strategy hedge fund.
Please email your CV to steven@aaaglobal.co.uk if you are interested in this role.
Quantitative Researcher in City of London employer: AAA Global
Contact Detail:
AAA Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Researcher in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your quantitative research projects, especially those involving machine learning and portfolio optimisation. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with Python and portfolio theory, as well as how you’ve tackled real-world challenges in your previous roles.
✨Tip Number 4
Don’t forget to apply through our website! It’s a great way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Quantitative Researcher in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of a Quantitative Researcher. Highlight your experience in systematic equity research and any relevant projects that showcase your skills in portfolio construction and optimisation.
Showcase Your Skills: Don’t forget to emphasise your advanced Python skills! Mention specific libraries you’ve used, like NumPy or pandas, and any machine learning applications you've worked on. This will help us see how you can contribute to our team.
Be Clear and Concise: When writing your application, keep it clear and concise. Use straightforward language to explain complex quantitative concepts, as this will demonstrate your ability to communicate effectively with PMs and other team members.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at AAA Global
✨Know Your Numbers
As a Quantitative Researcher, you’ll need to be comfortable with numbers and data. Brush up on your portfolio theory and optimisation techniques before the interview. Be ready to discuss how you've applied these concepts in previous roles, especially in relation to machine learning and risk management.
✨Showcase Your Python Skills
Since advanced Python skills are a must for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem or explain your thought process while coding. Practise using libraries like NumPy and pandas, and be ready to discuss any projects where you’ve used these tools.
✨Communicate Clearly
You’ll need to translate complex quantitative concepts into understandable terms for PMs. Practise explaining your past projects and methodologies in simple language. This will show that you can bridge the gap between technical research and practical application, which is crucial for this role.
✨Prepare for Scenario Analysis
Expect questions about stress testing and scenario analysis. Familiarise yourself with different risk models and be prepared to discuss how you would implement them in portfolio construction. Think of examples from your experience where you’ve successfully managed risk and optimised returns.
