Job Description
A leading quantitative trading firm is seeking a Quantitative Developer with strong Python programming skills and experience working with large-scale datasets. This role sits at the intersection of quantitative research and software engineering, building the tools and data pipelines that move trading ideas from research into production.
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The primary focus is writing efficient, production-quality Python for demanding data workflows. Experience with both pandas and Polars is highly desirable, alongside the ability to optimize performance as data volumes and computational demands grow.
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What you’ll do:
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- Develop and optimize Python systems for processing, transforming, and analyzing large-scale financial datasets.
- Build reliable data, feature, and signal pipelines that support quantitative research and live trading.
- Improve the speed, scalability, and memory efficiency of research workflows.
- Develop backtesting and simulation tools that account for market behavior and real-world trading constraints.
- Partner with researchers and engineers to translate research ideas into robust production systems.
- Investigate and resolve data quality, performance, and implementation issues across the research-to-production lifecycle.
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Qualifications:
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- 3–7 years of experience in quantitative development or a closely related software engineering role, ideally within a trading firm or systematic investment fund.
- Excellent Python coding skills, with a track record of building and maintaining production systems.
- Strong proficiency in pandas and/or Polars, ideally with practical experience in both.
- Familiarity with quantitative research, backtesting, and simulation workflows.
- Hands-on experience handling large datasets, including efficient data manipulation, pipeline development, and performance optimization.
- 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 collaborate effectively across research and engineering teams.
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Experience with low-latency trading systems is a plus.