Quantitative Developer - Python (Chicago)

A leading quantitative trading firm is seeking a Quantitative Developer with strong Python programming skills and experience supporting quantitative research and systematic trading . Working closely with traders and quantitative researchers, you’ll build the research tools, signal pipelines, and production systems that turn trading ideas into live strategies.

The role combines hands-on Python development with direct involvement in signal development, market data analysis, backtesting, and research implementation . You’ll help researchers evaluate ideas efficiently and translate promising results into reliable tools for trading desks. Experience with both pandas and Polars is highly desirable , alongside a strong understanding of the data and computational challenges involved in quantitative trading.

What you’ll do:

• Partner with traders and quantitative researchers to develop, evaluate, and implement trading signals and research ideas.
• Build Python tools for analyzing large-scale market datasets and supporting quantitative experimentation.
• Develop data, feature, and signal pipelines that connect research workflows with live trading.
• Build and enhance backtesting and simulation infrastructure that reflects market behavior and real-world trading constraints.
• Improve the speed, scalability, and memory efficiency of research workflows, enabling faster iteration on trading ideas.
• Translate research code into robust, production-quality systems deployed directly to trading desks.
• Investigate data quality and implementation issues that affect research validity and production reliability.

Qualifications:

• 3–7 years of experience in quantitative development, research engineering, or a closely related role, preferably within a proprietary trading firm, hedge fund, or systematic investment team .
• Excellent Python coding skills , with experience developing tools and production systems for quantitative research or trading.
• Strong proficiency in pandas and/or Polars, ideally with practical experience in both .
• Hands-on experience with market data, signal pipelines, backtesting, and simulation workflows .
• Experience processing large financial datasets and optimizing data-intensive research workflows.
• A strong foundation in probability, statistics, and time series analysis.
• An understanding of machine learning concepts and their application to systematic trading.
• The ability to work closely with traders and researchers, understand the reasoning behind a research idea, and translate it into an effective technical implementation.

Experience with low-latency trading systems is a plus.