Fintal Partners
Quantitative Developer
Chicago, Illinois, US
Role snapshot
Fintal Partners is hiring a Quantitative Developer on a full-time basis. This is a role based in Chicago, Illinois, US.
- Company
- Fintal Partners
- Location
- Chicago, Illinois, US
- Job type
- Full-time
- Posted
- Nov 6, 2026
Who this role suits
The listing does not state a required experience level here. Read the requirements in the description below and apply if they match your background.
How to prepare your application
- Match your résumé to the requirements in the description, using the same wording where it is honest to do so.
- Prepare a short example for each requirement you meet, and be ready to explain any you only partly meet.
- Read what Fintal Partners publishes about its product or team, so you can say clearly why this role and this company.
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About the role
Our client, a leading proprietary trading firm, is hiring a Quantitative Developer to take ownership of the entire journey from research to live production. The role sits between research and engineering, with fast feedback loops from first idea to live trading. You’ll build the systems that turn quantitative ideas into measurable edge, and see directly how your work drives results.
What You’ll Do:
- Build and maintain systems bridging research and production, enabling fast iteration from concept to deployment
- Develop realistic simulation and backtesting infrastructure that captures latency, market microstructure and real-world constraints
- Define, calculate and manage features across instruments, market regimes and time horizons
- Own feature and signal pipelines, keeping delivery clean and consistent from research through to execution
- Help optimize strategies, weighing expected performance against practical constraints
- Troubleshoot issues end-to-end across research and execution
What You’ll Bring:
- 3–7 years in quantitative software development, ideally at a trading firm or systematic fund
- Strong production Python, including data analysis tools (pandas, polars or similar)
- Solid foundation in probability, statistics and time series analysis; familiarity with back-testing and simulation frameworks
- Good understanding of ML as applied to systematic strategies, from research to production
- Low-latency systems experience is a plus
- Comfortable working across both research and engineering teams