Quant Research Intern: ML-Driven Trading Ideas

Trading Interview; Greater London; Hybrid; GBP 17,000 - 28,000; Full time

Job summary

DRW is offering a Quantitative Research Intern position in London, working on problems from a trading environment using statistical methods, ML techniques and derivatives pricing concepts. You will access the team’s research infrastructure for simulation, back-testing and validation of models.

The role emphasizes collaboration with traders and researchers, with exposure to multi-asset strategies across geographies.

Qualifications

• Pursuing a Bachelor's, Master's or PhD in a technical discipline graduating between December 2027 and October 2028.
• Proficiency in Python and the Python ML stack (e.g., NumPy, Pandas, scikit-learn).
• Proficient programming with experience exploring large datasets.
• Strong analytical and problem solving skills with solid statistics knowledge.
• Knowledge of probability theory, stochastic calculus and numerical methods such as Monte Carlo.
• NLP or HPC exposure is a plus.
• Excellent written and verbal communication to report results and methodologies.
• Publications in top journals a plus.

Responsibilities

• Create practical solutions to problems in the trading environment on systematic desks.
• Conduct statistical analysis of market data and relationships across assets.
• Apply mathematical modeling and ML techniques to identify opportunities.
• Collaborate with traders and researchers to build and refine research infrastructure.

Skills

Python

Statistics

Problem solving

Communication

NLP

High-Performance Computing

Education

Technical degree (BSc/MSc/PhD)

Tools

NumPy

Pandas

Scikit-learn

Job description

DRW is offering a Quantitative Research Intern position in London, working on problems from a trading environment using statistical methods, ML techniques and derivatives pricing concepts. You will access the team’s research infrastructure for simulation, back-testing and validation of models.

The role emphasizes collaboration with traders and researchers, with exposure to multi-asset strategies across geographies.