Quant Research Intern in York

Job Overview

We are looking for a Quantitative Research Intern to join our fast-growing digital asset trading team. This role offers the opportunity to apply cutting-edge machine learning and statistical modelling to identify inefficiencies and uncover alpha in global crypto markets. You will work closely with experienced researchers and developers to design, implement, and optimise systematic trading strategies in a data-intensive, high-performance environment.

Location

London / Hong Kong

Key Responsibilities

• Research and develop mid- to high-frequency systematic trading strategies using advanced statistical, econometric, and machine learning techniques.
• Analyse large-scale, real-time market data to detect microstructure patterns, market inefficiencies, and predictive signals.
• Backtest, optimise, and monitor strategies across simulated and live trading environments.
• Collaborate with the engineering team to enhance trading infrastructure, data pipelines, and research tools for scalability and robustness.
• Contribute to the continuous evolution of the research framework, including feature engineering, simulation engines, and model evaluation processes.
• Maintain rigorous risk control and ensure all research and trading activities comply with internal and regulatory standards.

Requirements

• Master’s or PhD in Finance, Mathematics, Physics, Statistics, Engineering, or Computer Science.
• Strong grasp of linear algebra, probability, statistics, and regression analysis.
• Demonstrated interest or experience in quantitative research or systematic trading.
• Proficiency in Python and familiarity with C++ or other object-oriented programming languages.
• Experience with machine learning models (e.g. gradient boosting trees, deep learning) for signal discovery or time-series prediction is highly desirable.
• Mandarin fluency is a plus.

What we offer:

• Exposure to a high-impact, data-driven trading environment.
• Mentorship from senior quantitative researchers and technologists.
• Opportunity to apply ML-driven approaches to real-world trading problems and alpha generation.