A leading systematic hedge fund is seeking a Machine Learning Quantitative Researcher to research and develop ML-driven investment strategies.
Working within a highly quantitative research environment, you will apply machine learning, statistics and mathematical modelling to large-scale financial datasets, uncover predictive signals and translate research into systematic trading strategies.
The role is well suited to someone who combines strong machine learning expertise with rigorous statistical thinking and an interest in solving challenging problems in financial markets.
Key Responsibilities
- Research and develop machine learning-driven alpha signals and systematic trading strategies.
- Apply advanced ML and statistical techniques to large, complex and noisy financial datasets.
- Develop predictive models across financial markets using both traditional and alternative datasets.
- Conduct rigorous backtesting and out-of-sample validation to assess model robustness and generalisation.
- Research techniques across areas such as deep learning, probabilistic modelling, time-series modelling, representation learning and reinforcement learning.
- Identify nonlinear relationships and patterns that can provide differentiated investment signals.
- Analyse model behaviour across changing market regimes and conditions.
- Work closely with quantitative researchers, portfolio managers and engineers to turn research into production-ready strategies.
- Continuously evaluate developments in machine learning and determine their applicability to systematic investing.
Candidate Profile
The successful candidate will typically have:
- Strong expertise in machine learning, statistics and quantitative modelling.
- Excellent foundations in probability, statistics, linear algebra and optimisation.
- Experience developing and validating predictive models using real-world datasets.
- Strong understanding of experimental design, overfitting, model selection and out-of-sample testing.
- Excellent programming skills, particularly in Python.
- Experience with ML frameworks such as PyTorch, JAX or TensorFlow.
- Strong research instincts and an ability to independently investigate open-ended quantitative problems.
- An interest in applying cutting-edge ML research to financial markets and alpha generation.
Previous experience within a hedge fund or systematic trading environment is advantageous, but exceptional candidates from technology, AI research or academia may also be considered.
Relevant Research Areas
Experience in one or more of the following would be valuable:
- Statistical Machine Learning
- Deep Learning
- Time-Series Modelling
- Probabilistic & Bayesian Modelling
- Representation Learning
- Reinforcement Learning
- Generative Modelling
- Optimisation
- Alternative Data
- Signal Research
- Portfolio Modelling
Depth of expertise and research quality are more important than experience across every area.
Academic Background
A strong academic background in a highly quantitative discipline is preferred, such as:
- Machine Learning / Artificial Intelligence
- Computer Science
- Statistics
- Mathematics
- Physics
- Engineering
- Operations Research
A Master's or PhD from a strong academic institution is highly desirable, particularly for candidates with research experience in machine learning, statistics or related quantitative disciplines.
Why Join?
This is an opportunity to apply sophisticated machine learning research directly to systematic investing and alpha generation.
You will work alongside experienced quantitative researchers in a highly technical environment, with access to significant datasets and computational resources. The role offers substantial research ownership and the opportunity to take ideas from initial hypothesis through modelling, validation and ultimately into live systematic strategies.
