Groundbreaking Quantitative Researcher - ML & Time Series

Hikarosquantlab; Greater London; Híbrido; GBP 90 000 - 150 000; Tempo integral

Resumo da oferta

Hikaros Quant Lab in London is assembling a small, elite research team and seeks a Quantitative Researcher with strong maths, statistics and ML to join at an unusually early stage. You will develop models, test hypotheses and drive experiments, shaping the research organisation from the ground up.

You may come from mathematics, statistics, physics, CS, or other research-intensive fields; no finance background required.

Qualificações

• Exceptional mathematical and statistical problem-solving ability.
• Strong programming skills.
• Ability to conduct independent research and move from theory to implementation.
• Track record of academic or competitive excellence.

Responsabilidades

• Develop statistical and mathematical models for predictive problems.
• Apply machine learning and deep learning to large-scale datasets.
• Investigate time-series prediction and representation learning.
• Design experiments and test hypotheses through empirical research.
• Build simulation and backtesting frameworks.
• Work with large-scale data and computational infrastructure.
• Translate promising research into robust, systematic investment strategies.
• Explore new applications of AI, optimisation and scientific computing.

Conhecimentos

Mathematics

Statistics

Computer Science

Machine Learning

Physics

Operations Research

Applied Mathematics

Quantitative disciplines

Formação académica

PhD or PhD student in a quantitative field

Master's degree in a quantitative field

Ferramentas

Python

TensorFlow/PyTorch

CUDA/GPUs

Descrição da oferta de emprego

Hikaros Quant Lab in London is assembling a small, elite research team and seeks a Quantitative Researcher with strong maths, statistics and ML to join at an unusually early stage. You will develop models, test hypotheses and drive experiments, shaping the research organisation from the ground up.

You may come from mathematics, statistics, physics, CS, or other research-intensive fields; no finance background required.