Python Quantitative Developer - Build High-Impact Trading Systems

Citadel; City of Rochester (NY); On-site; USD 200,000 - 300,000; Full time

Benefits offered by this job

Discretionary incentive compensation

Medical and life insurance

Retirement and tax-free savings plans

Job summary

Citadel is hiring Quantitative Developers in the United States to blend software engineering with quantitative analysis, building production systems that accelerate research and decision-making across investment teams.

You will work alongside researchers, portfolio managers, and traders to translate models into scalable software, develop data pipelines, and create tools that enable rapid testing and insight extraction from large datasets.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or related STEM field or equivalent experience.
• Exceptional Python programming skills and strong CS fundamentals.

Responsibilities

• Partner with researchers, PMs, traders, and engineers to understand workflows.
• Translate research ideas and models into reliable production software.
• Build and improve high-performance data pipelines and analytical infrastructure.
• Develop tools and prototypes to test ideas and extract insights from data.
• Solve technically challenging problems in a fast-moving environment with business impact.
• Learn from industry leaders and investment professionals.

Skills

Python programming

Data structures & algorithms

Large datasets handling

Communication skills

Education

Bachelor's degree in Computer Science or related STEM

Job description

Citadel is hiring Quantitative Developers in the United States to blend software engineering with quantitative analysis, building production systems that accelerate research and decision-making across investment teams.

You will work alongside researchers, portfolio managers, and traders to translate models into scalable software, develop data pipelines, and create tools that enable rapid testing and insight extraction from large datasets.