Quantitative Research Intern: ML & Trading Analytics

P2P; Singapore; On-site; SGD 16,740 - 23,436; Full time

Job summary

DRW in Singapore invites a Quantitative Research Intern to tackle challenging problems in a trading environment, leveraging statistical methods, ML techniques, and derivatives pricing theory. You will use a custom research stack for simulation, back-testing, and model validation, while enjoying city-life social events and a collaborative team across geographies.

Ideal candidates are pursuing a technical degree with strong Python and statistics background, capable of handling large datasets and

Qualifications

• Pursuing a Bachelor's, Master's or PhD in a technical discipline with a focus on Statistics, Optimization, Machine Learning, Artificial Intelligence, Quantitative Finance or related fields graduating between December 2027 and August 2028
• Proficient programming skills with experience exploring large datasets
• Excellent written and verbal communication skills to report research results as well as methodologies
• Some exposure to NLP and/or HPC is a plus

Responsibilities

• Create practical solutions to problems presented in the trading environment on either a systematic equity trading desk or a fixed income options desk
• Conduct statistical analysis of market data, historical trends, and relationships across multiple asset classes
• Formulate and apply mathematical modeling, quantitative methods and machine learning techniques to identify and capture trading opportunities
• Work closely with traders and researchers to build and refine research infrastructure and tools

Skills

Python programming

Statistics

Machine Learning

Data analysis

Communication skills

Education

Bachelor/Master/PhD in Statistics/ML/Quant Finance

Tools

NumPy

Pandas

scikit-learn

Job description

DRW in Singapore invites a Quantitative Research Intern to tackle challenging problems in a trading environment, leveraging statistical methods, ML techniques, and derivatives pricing theory. You will use a custom research stack for simulation, back-testing, and model validation, while enjoying city-life social events and a collaborative team across geographies.

Ideal candidates are pursuing a technical degree with strong Python and statistics background, capable of handling large datasets and