Publisher
source

Optiver

Just Landed

Quant Researcher (PhD) - Internship and Fulltime (Chicago, Austin, New York City) Optiver in United States

Degree Level

PhD

Field of study

Computer Science

Funding

Highly competitive compensation packages tailored to internship and full-time opportunities.

Deadline

Apr 20, 2027

Country flag

Country

United States

University

Optiver

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Keywords

Computer Science
Deep Learning
Mathematics
Finance
Predictive Modeling
Time Series Analysis
Market Microstructure
Statistics
Statistical Modelling
Financial Risk Management
ML

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About this position

This opening is for a Quantitative Researcher role at Optiver, with opportunities available for both internship and full-time hiring across the Chicago, Austin, and New York City offices.

The role sits inside a highly applied research environment focused on global financial markets. You would work alongside researchers, engineers, and traders to investigate large-scale market and order-flow data, frame research questions, test hypotheses, and build models that can be evaluated in live trading environments.

The work combines statistical thinking, machine learning, and quantitative finance. The researcher is expected to develop predictive systems, study market behavior, and design models for pricing, forecasting, and risk management. The posting also highlights modern AI-enabled research workflows and deep learning tools as part of the broader research process.

For internship candidates, the role includes an apprentice-style learning model led by Optiver’s Education team, with work on a project aligned to current business needs. For full-time candidates, the position includes Global Optiver Academy, an onboarding program for graduates before moving into a team.

Optiver describes itself as a technology- and research-driven trading firm where scientists, engineers, mathematicians, and traders collaborate on ideas that shape how the firm understands and trades global markets. The environment emphasizes rapid experimentation, iteration, mentorship, and applied problem solving.

Funding details

Highly competitive compensation packages tailored to internship and full-time opportunities.

What's required

Currently pursuing or recently completed a PhD in Statistics, Computer Science, Machine Learning, Mathematics, or a related STEM field, with outstanding academic achievements. Applicants should have a solid foundation in mathematics, probability, and statistics, excellent research, analytical, and modeling skills, experience applying machine learning methods to real-world research problems, proficiency in any programming language, strong interest in a fast-paced collaborative environment, and fluent English with strong written and verbal communication skills. Eligibility for internship or full-time opportunities depends on expected graduation date and availability.

How to apply

Submit an application through the posted MathJobs link. Ensure your materials reflect your PhD background, quantitative research experience, and machine learning skills. Check your eligibility for internship or full-time consideration based on graduation date and availability.

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Review drafts and confirm requirements with the university.