Quantitative Research Intern

Drweng; Chicago (IL); On-site; USD 250,000 - 300,000; Full time

Job summary

DRW is seeking a Quantitative Research Intern to tackle challenging problems in a trading environment, leveraging statistics, ML, and derivatives pricing theory. You’ll work with a team on non-latency sensitive opportunities across asset classes and gain exposure to research infrastructure, ML techniques, and back-testing.

Ideal candidates are pursuing technical degrees with graduation between December 2027 and August 2028, have Python ML stack experience, and strong communication skills for

Qualifications

• Pursuing a Bachelor’s, Master’s or PhD in a technical discipline with a focus on Statistics, Optimization, Machine Learning, AI, Quantitative Finance or related fields graduating between December 2027 and August 2028.
• Proficiency in Python programming with experience in the Python ML stack (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 algorithms (e.g., finite differences, Monte Carlo).
• Some exposure to NLP and/or High‑Performance Computing is a plus.
• Excellent written and verbal communication to report results and methodologies.
• Bonus if published in top journals focusing on NLP or HPC.

Responsibilities

• Create practical solutions in a trading environment on systematic equity or fixed income desks.
• Conduct statistical analysis of market data and relationships across asset classes.
• Apply mathematical modeling and ML to identify and capture trading opportunities.
• Collaborate with traders and researchers to build and refine research infrastructure and tools.
• Engage in learning experiences and mentorship through structured projects.

Skills

Python programming

Statistics knowledge

Machine learning

Data analysis

Communication skills

NLP exposure

High-Performance Computing

Large dataset handling

Education

Bachelor’s/Master’s/PhD in Statistics/Math/CS

Tools

Python stack (NumPy, pandas, scikit-learn)

Monte Carlo simulation

Finite differences

NLP libraries

Job description

Our formula for success is to hire exceptional people, encourage their ideas and reward their results.

As a Quantitative Research Intern you will have an opportunity to solve challenging problems arising in a trading environment while utilizing the latest statistical scientific algorithms, machine learning techniques and derivatives pricing theory. The teams focus on non‑latency sensitive investment opportunities and multi‑asset class derivatives strategies across geographies. Our teams emphasize cutting‑edge innovative scientific research and collaboration, allowing you to gain a deeper understanding of quantitative trading. You will find great minds with diverse backgrounds, who are passionate about cultivating new ideas and exploring ways to bring them to life. You will use the team’s custom research infrastructure for simulation, back‑testing, and validation of the proposed models. While your days will have you engrossed with complex technology projects, your evenings will be spent exploring the city with organized social events to truly discover what it’s like to live and work in Chicago or New York City.

How you will make an impact…

• 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

What you bring to the team…

• Are 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
• Proficiency in Python programming experience using the Python machine learning stack: numpy, pandas, scikit‑learn, etc.
• Proficient programming skills with experience exploring large datasets
• Strong analytical and problem‑solving skills including a solid foundation of statistics knowledge
• Working knowledge of probability theory, stochastic calculus and numerical algorithms such as finite differences, Monte Carlo simulation, etc.
• Some exposure to Natural Language Processing and/or High‑Performance Computing is a plus
• Excellent written and verbal communication skills to report research results as well as methodologies
• Added bonus if you have been published in a top tier journal focusing on Natural Language Processing or High‑Performance Computing

What to expect during the internship

• Meaningful projects: Each project, advised by a trader, promotes a comprehensive learning experience and provides you with real‑world work experience.
• Community: Throughout the summer, we host a variety of educational, social and team‑building activities to explore the city, foster friendships and camaraderie.
• Housing: DRW provides fully furnished apartments located close to the office – making your morning commute as easy as possible.
• Mentorship: You’ll build a professional relationship with an experienced mentor in your field. Mentors and mentees meet to discuss goals, challenges and professional development and explore the city together at our mentor outings.
• Education: As the trading industry continually evolves, both in terms of new products and transaction methods, the future will present us with unique opportunities and challenges. You’ll complete an options course taught by an experienced trader and participate in a technology immersion course to better understand how technology and trading intersect.

The annual base salary range for this position is $250,000 to $300,000 depending on the candidate’s experience, qualifications, and relevant skill set.

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