Graduate Quantitative Researcher, PhD (2027 Start)

optiverus; New York (NY); On-site; USD 90,000 - 130,000; Part time

Benefits offered by this job

Global profit-sharing

Performance-based bonus

401(k) match

Comprehensive health coverage

Vacation days

Job summary

Optiver is seeking PhD students or graduates to join its quantitative research internship program in New York. You’ll join Global Optiver Academy, gain a foundation in trading systems, risk management, and AI-enabled workflows, and work closely with researchers and traders.

During the internship, you’ll develop predictive models, analyze market data, and test pricing and forecasting models using modern ML techniques.

Qualifications

• PhD in Statistics, Computer Science, Machine Learning, Mathematics, or a related STEM field with outstanding academic achievements.
• Expected to graduate by mid-2027 and available to start full-time employment upon graduation.
• Solid foundation in mathematics, probability, and statistics.
• Excellent research, analytical, and modeling skills.
• Experience applying machine learning methods to real-world research problems, such as time-series analysis, prediction, forecasting, pattern recognition, optimization, or decision-making.
• Proficiency in any programming language.
• Strong interest in working in a fast-paced, collaborative environment.
• Fluent in English with strong written and verbal communication skills.

Responsibilities

• Develop predictive models and machine learning systems to better understand market behavior and identify trading opportunities.
• Analyze large-scale market and order-flow data to uncover signals, evaluate hypotheses, and improve trading performance.
• Build and test statistical and stochastic models for pricing, forecasting, and risk management.
• Apply modern research techniques, including deep learning and AI-enabled workflows, to accelerate discovery and improve research efficiency.

Skills

Programming

Time-series

Forecasting

Research

Education

PhD in Statistics/CS/ML/Math or related STEM

Tools

Python

ML frameworks

Job description

What You'll Do:

Your onboarding

You’ll participate in Global Optiver Academy , a structure onboarding program for graduates. It gives new joiners a shared foundation in how Optiver trades, builds systems, and manages risk before they move into their teams. As part of your onboarding, you’ll gain exposure to the AI tools and technologies that support research and development across the business.

Your responsibilities

• Develop predictive models and machine learning systems to better understand market behavior and identify trading opportunities
• Analyze large-scale market and order-flow data to uncover signals, evaluate hypotheses, and improve trading performance
• Build and test statistical and stochastic models for pricing, forecasting, and risk management
• Apply modern research techniques, including deep learning and AI-enabled workflows, to accelerate discovery and improve research efficiency

What You'll Get:

You’ll join a culture of collaboration, continuous improvement, and excellence, surrounded by curious thinkers and creative problem-solvers. Together, you’ll tackle some of the toughest challenges in the financial markets by leveraging cutting-edge machine learning research to develop innovative, real-world solutions.

• The opportunity to work alongside best-in-class professionals from over 40 different countries
• Highly competitive compensation package
• Global profit-sharing pool and performance-based bonus structure
• 401(k) match up to 50%
• Comprehensive health, mental, dental, vision, disability, and life coverage
• 25 paid vacation days alongside market holidays
• Extensive office perks, including breakfast, lunch, snacks, regular social events, clubs, sporting leagues, and more

What To Expect :

As part of our assessment process, you may be invited to participate in a multi-day, on-site evaluative program. Through hands-on workshops, technical discussions, and direct exposure to our researchers and traders, you’ll gain insight into how research is applied at Optiver and how PhD students transition successfully into industry. Attendance and successful completion of this program may be required to receive an internship offer.

Who You Are:

• PhD in Statistics, Computer Science, Machine Learning, Mathematics, or a related STEM field, with outstanding academic achievements
• Expected to graduate by mid-2027 and available to start full-time employment upon graduation
• Solid foundation in mathematics, probability, and statistics
• Excellent research, analytical, and modeling skills
• Experience applying machine learning methods to real-world research problems, such as time-series analysis, prediction, forecasting, pattern recognition, optimization, or decision-making
• Proficiency in any programming language
• Strong interest in working in a fast-paced, collaborative environment
• Fluent in English with strong written and verbal communication skills

Who We Are:

Optiver is a leading technology- and research-driven trading firm. Our teams of scientists, engineers, mathematicians, and traders work side by side to develop, test, and scale ideas that shape how we understand and trade global markets. Powered by a global platform built for rapid experimentation and iteration, we combine the scientific rigor of a research institution with the pace of a technology company.

Our differences are our edge. Optiver does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, physical or mental disability, or other legally protected characteristics.

Optiver is supportiv e of US immigration sponsorship for this role .

Optiver has a global application re-apply policy for our intern and graduate roles. If you have completed an online assessment or interviewed for a quantitative graduate or internship role at any Optiver location in the past 8 months, please note that you are not yet eligible to reapply. We welcome you to re-apply to after the 8-month cool off period.

Below is the expected base salary for this position. This position will a