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- Tasks: Conduct cutting-edge research in machine learning to model market behaviour and generate alpha.
- Company: Leading global hedge fund with a data-driven approach and over $35B AUM.
- Benefits: Competitive salary, dynamic work environment, and opportunities for impactful research.
- Why this job: Make a real impact on live investment strategies using advanced ML techniques.
- Qualifications: PhD or Master’s in a quantitative field with strong programming skills in Python.
- Other info: Collaborative team culture with excellent career growth potential.
The predicted salary is between 43200 - 72000 £ per year.
A leading global hedge fund with over $35B AUM is seeking a Quantitative Machine Learning Researcher to join its growing systematic research group. The team applies advanced ML and statistical techniques to develop predictive models, identify alpha signals, and optimize portfolio construction across global markets. This is a unique opportunity for a top-tier researcher with strong academic credentials and hands-on technical skills to work in a world-class, data-driven environment with direct impact on live investment strategies.
Key Responsibilities:
- Conduct research into machine learning and statistical methods to model market behaviour and generate alpha.
- Design and test predictive features using large, diverse, and noisy datasets across equities, futures, and macro products.
- Contribute to signal validation, model explainability, and robustness testing for production-ready strategies.
- Collaborate with portfolio managers, engineers, and data scientists to integrate models into live trading frameworks.
- Explore new data sources and ML techniques to expand signal coverage and performance.
Core Skills & Experience:
- PhD (or Master’s with 1–2 years of experience) in a quantitative discipline such as Machine Learning, Computer Science, Statistics, Physics, Mathematics, or Engineering.
- Degree from a top 20 global university (e.g., Oxford, Cambridge, MIT, Stanford, Harvard, Princeton, ETH, Imperial, etc.).
- Strong background in machine learning, deep learning, or reinforcement learning.
- Hands-on programming experience in Python (PyTorch, TensorFlow, NumPy, Pandas, Scikit-learn).
- Solid understanding of statistical modeling, time-series analysis, and data preprocessing.
- Familiarity with financial markets, quantitative trading, or asset pricing concepts preferred but not required.
- Self-directed researcher with excellent problem-solving skills and a strong desire to work in a high-performance team.
This is a rare opportunity to transition cutting-edge research into live trading impact — combining academic rigor with real-world scalability in a collaborative, world-class environment.
Machine Learning Researcher in London employer: HWTS Global
Contact Detail:
HWTS Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Researcher in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the finance and machine learning sectors on LinkedIn. Join relevant groups and participate in discussions to get your name out there and show off your expertise.
✨Tip Number 2
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your past projects and how you’ve applied machine learning techniques to solve real-world problems. Practice coding challenges to keep your skills sharp!
✨Tip Number 3
Don’t just apply through job boards; head over to our website and submit your application directly. This shows initiative and can help you stand out from the crowd. Plus, we love seeing candidates who take that extra step!
✨Tip Number 4
Stay updated on industry trends and advancements in machine learning. Follow relevant blogs, attend webinars, and read research papers. This knowledge will not only help you in interviews but also demonstrate your passion for the field.
We think you need these skills to ace Machine Learning Researcher in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Machine Learning Researcher role. Highlight your academic credentials, programming skills, and any relevant projects that showcase your expertise in ML and statistics.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how your background makes you a perfect fit for our team. Don’t forget to mention any specific projects or research that relate to the job description.
Showcase Your Technical Skills: Since this role requires hands-on programming experience, be sure to include examples of your work with Python and any libraries like PyTorch or TensorFlow. If you've worked on predictive models or data analysis, share those experiences to demonstrate your capabilities.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at HWTS Global
✨Know Your Stuff
Make sure you brush up on your machine learning concepts and statistical methods. Be ready to discuss your research and how it applies to market behaviour. They’ll want to see that you can translate complex ideas into practical applications.
✨Show Off Your Coding Skills
Since hands-on programming experience is key, be prepared to demonstrate your Python skills. Bring examples of projects you've worked on using libraries like PyTorch or TensorFlow, and be ready to solve coding problems on the spot.
✨Understand the Financial Landscape
Even if you’re not a finance whiz, having a basic understanding of financial markets and quantitative trading will set you apart. Familiarise yourself with key concepts and be ready to discuss how your work could impact investment strategies.
✨Collaborate and Communicate
This role involves working closely with portfolio managers and data scientists, so highlight your teamwork skills. Prepare examples of past collaborations and how you effectively communicated complex ideas to non-technical stakeholders.
