AI/ML Quant Specialist

Selby Jennings; City Of London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Selby Jennings is seeking an exceptional AI/ML Specialist to join its research team, at the intersection of machine learning, statistics, probability and quantitative research. The role focuses on applying advanced modelling techniques to complex financial market problems.

The candidate will work with quantitative researchers, traders and engineers to develop models, run rigorous experiments, and push ideas from research to production in a highly collaborative environment.

Qualifications

• Strong foundations in probability theory, statistics, linear algebra and optimisation.
• Experience developing sophisticated predictive or probabilistic models.
• Ability to design rigorous experiments and validate model generalisation.
• Experience working with large, complex or noisy datasets.
• Strong programming ability, particularly in Python.

Responsibilities

• Research and develop advanced machine learning and statistical models for quantitative and trading applications.
• Apply techniques from statistics, probability, deep learning and probabilistic modelling to complex real-world problems.
• Develop predictive models using large-scale, high-dimensional and noisy datasets.
• Explore new approaches across areas such as time-series modelling, representation learning, reinforcement learning, Bayesian methods and generative modelling.
• Design rigorous experiments to evaluate model performance, robustness and generalisation.
• Work closely with quantitative researchers and traders to identify areas where ML can improve research and decision-making.
• Develop scalable research workflows and collaborate with engineers to move successful models towards production.
• Stay current with developments across machine learning, statistics and AI, assessing their applicability to quantitative trading.
• Contribute to a highly collaborative, research-driven environment where ideas are tested quickly and objectively.

Skills

Machine learning

Statistics

Probability

Experimental design

Data analysis

Optimization

Python

Education

Master's degree or PhD in a quantitative field

Tools

PyTorch

JAX

TensorFlow

Python

Job description

A leading proprietary trading firm is seeking an exceptional AI/ML Specialist to join its research team. This role sits at the intersection of machine learning, statistics, probability and quantitative research, applying advanced modelling techniques to complex problems within financial markets.

The successful candidate will combine strong ML expertise with exceptional mathematical foundations and a rigorous approach to experimentation. You will work alongside quantitative researchers, traders and engineers to develop innovative models and extract meaningful signals from large, noisy datasets.

Key Responsibilities

• Research and develop advanced machine learning and statistical models for quantitative and trading applications.
• Apply techniques from statistics, probability, deep learning and probabilistic modelling to complex real-world problems.
• Develop predictive models using large-scale, high-dimensional and noisy datasets.
• Explore new approaches across areas such as time-series modelling, representation learning, reinforcement learning, Bayesian methods and generative modelling.
• Design rigorous experiments to evaluate model performance, robustness and generalisation.
• Work closely with quantitative researchers and traders to identify areas where ML can improve research and decision-making.
• Develop scalable research workflows and collaborate with engineers to move successful models towards production.
• Stay current with developments across machine learning, statistics and AI, assessing their applicability to quantitative trading.
• Contribute to a highly collaborative, research-driven environment where ideas are tested quickly and objectively.

Candidate Profile

We are looking for candidates with exceptional technical and mathematical ability, particularly across machine learning, statistics and probability.

The ideal candidate will have:

• Strong expertise in modern machine learning and statistical modelling.
• Excellent foundations in probability theory, statistics, linear algebra and optimisation.
• Experience developing sophisticated predictive or probabilistic models.
• Strong understanding of experimental design, statistical validation and model generalisation.
• Experience working with large, complex or noisy datasets.
• Strong programming ability, particularly in Python.
• Experience with ML frameworks such as PyTorch, JAX or TensorFlow.
• Ability to approach open-ended research problems with mathematical and scientific rigour.
• Strong communication skills and the ability to collaborate with researchers, traders and engineers.

Relevant Technical Expertise

Experience in several of the following would be particularly valuable:

• Statistical Learning
• Probability & Stochastic Modelling
• Bayesian Inference
• Deep Learning
• Time-Series Modelling
• Representation Learning
• Reinforcement Learning
• Generative Models
• Optimisation
• Monte Carlo Methods
• PyTorch / JAX
• Python
• Large-scale data analysis

Candidates do not need experience across every area. Exceptional depth in machine learning combined with particularly strong statistics and probability skills is more important.

Academic Background

A strong academic background in a highly quantitative discipline is preferred, including:

• Machine Learning / Artificial Intelligence
• Statistics
• Mathematics
• Computer Science
• Physics
• Electrical Engineering
• Operations Research

A Master's or PhD from a strong academic institution would be highly attractive, particularly where the candidate has undertaken research involving advanced machine learning, statistics or probabilistic modelling.

Why Join?

This is an opportunity to work in an environment where mathematical rigour, machine learning and real-world impact are closely connected.

You will have access to challenging datasets, strong computational resources and highly technical colleagues, with the freedom to explore sophisticated research ideas and apply them to some of the most difficult quantitative problems in financial markets.