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.