ML Quant Researcher - Selby Jennings

A leading systematic hedge fund is seeking aMachine 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.

• Research and developmachine 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 asdeep 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.

The successful candidate will typically have:

• Strong expertise inmachine 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 inPython.
• Experience with ML frameworks such asPyTorch, 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 fromtechnology, AI research or academia may also be considered.

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.

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

AMaster'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.

This is an opportunity to apply sophisticated machine learning research directly tosystematic 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.