Quantitative Developer - Python

ittihad medical centre; Chicago (IL); Sur place; USD 200 000 - 225 000; Plein temps

Résumé du poste

IMC Trading is seeking a Quantitative Developer to own the full path from research to production. This role blends research and engineering, with tight feedback loops from ideation to live trading.

You will build systems that turn quantitative insights into measurable edge, with direct visibility into how your work creates impact. Base salary range is provided below.

Qualifications

• 3-7 years of experience in quantitative software development.
• Strong production experience in Python, including data analysis workflows (pandas, polars, or similar).
• Strong grounding in probability, statistics, and time series analysis; familiarity with backtesting and simulation frameworks.
• Solid understanding of ML concepts as applied to systematic strategies, from research through production.

Responsabilités

• Build and maintain systems that span research and production, enabling rapid iteration from idea to production.
• Design high-fidelity simulation and backtesting infrastructure that models latency, microstructure, and real-world constraints.
• Define, compute, and curate features across instruments, regimes, and time horizons.
• Own feature and signal pipelines, ensuring clean, consistent delivery from research to execution.
• Contribute to strategy optimization, balancing expected performance with real-world constraints.
• Debug issues end-to-end across research and execution.

Connaissances

Python

Pandas/Polars

Time series analysis

Statistics

Machine learning concepts

Low-latency systems

Outils

Backtesting frameworks

Latency optimization

Description du poste

IMC is looking for a Quantitative Developer to own the full path from research to production. This role blends research and engineering, with tight feedback loops from ideation to live trading. You will build the systems that turn quantitative insights into measurable edge, with direct visibility into how your work creates impact.

Your Core Responsibilities:

• Build and maintain systems that span research and production, enabling rapid iteration from idea to production
• Design high-fidelity simulation and backtesting infrastructure that models latency, microstructure, and real-world constraints
• Define, compute, and curate features across instruments, regimes, and time horizons
• Own feature and signal pipelines, ensuring clean, consistent delivery from research to execution
• Contribute to strategy optimization,balancing expected performance with real-world constraints
• Debug issues end-to-end across research and execution

Your Skills and Experience:

• 3-7 years of experience in quantitative software development, preferably at a trading firm or systematic fund
• Strong production experience in Python, including data analysis workflows (pandas, polars, or similar)
• Strong grounding in probability, statistics, and time series analysis; familiarity with backtesting and simulation frameworks
• Solid understanding of ML concepts as applied to systematic strategies, from research through production
• Experience with low-latency systems is valuable
• Ability to work fluidly across research and engineering teams

Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance.Please visit Benefits - US | IMC Trading for more comprehensive information.

Salary Range

$200,000 - $225,000 USD

About Us

IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.