Quant Research Intern: ML-Driven Trading

Trading Interview; New York (NY); On-site; USD 250,000 - 300,000; Full time

Benefits offered by this job

Housing provided

Mentorship program

Social events

Educational courses

Job summary

DRW in Chicago and New York City seeks a Quantitative Research Intern to tackle challenging problems using statistics, machine learning, and derivatives pricing theory. You will work on non-latency sensitive opportunities across multi-asset classes and collaborate with traders and researchers.

You are pursuing a technical degree with graduation between December 2027 and August 2028, and have Python ML stack experience (NumPy, pandas, scikit-learn) with strong data analytics fundamentals.

Qualifications

• Pursuing a technical degree with expected graduation Dec 2027–Aug 2028.
• Strong Python ML stack experience (NumPy, pandas, scikit-learn).
• Experience analyzing large datasets.
• Solid foundation in statistics and probability theory.
• Familiarity with stochastic calculus and numerical methods.
• Nice to have NLP or HPC exposure.
• Excellent written and verbal communication.
• Bonus for publications in NLP/HPC journals.

Responsibilities

• Create practical solutions to trading problems on equity or fixed income desks.
• Conduct statistical analysis of market data across asset classes.
• Apply mathematical modeling and ML to identify trading opportunities.
• Collaborate with traders and researchers to build research tools.

Skills

Python programming

NumPy

Pandas

scikit-learn

Statistics

Machine learning

Big data

Communication

NLP

HPC

Problem solving

Education

Technical degree (Bachelor/Master/PhD)

Job description

DRW in Chicago and New York City seeks a Quantitative Research Intern to tackle challenging problems using statistics, machine learning, and derivatives pricing theory. You will work on non-latency sensitive opportunities across multi-asset classes and collaborate with traders and researchers.

You are pursuing a technical degree with graduation between December 2027 and August 2028, and have Python ML stack experience (NumPy, pandas, scikit-learn) with strong data analytics fundamentals.