Job Title: Quantitative Developer
Location: Chicago, IL – 100% REMOTE
Long Term Contract – W2 / C2C
Job Summary: We are seeking a highly experienced and adaptable Quantitative Developer to join our client's team in Chicago. This role requires a unique blend of strong quantitative and technical skills, deep financial domain knowledge, and a proactive learning attitude. You will collaborate closely with quantitative researchers, risk managers, and portfolio management teams to design, develop, and optimize analytical tools and models in a high-performance computing environment.
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Design and implement production-grade code that translates quantitative models into efficient and scalable solutions.
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Work closely with Quantitative Research, Risk, and Equity Portfolio Management teams to support model development and risk analytics.
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Contribute across the software development lifecycle including requirements analysis, coding, testing, and deployment.
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Build solutions using a wide array of technologies including Python, PySpark, R, Java, and cloud-based big data platforms like Databricks.
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Develop in both real-time and batch-oriented architectures.
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Employ Test-Driven Development (TDD) to ensure code quality, scalability, and maintainability.
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Continuously explore and integrate modern technologies and industry best practices into development processes.
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Communicate complex quantitative and technical concepts effectively to non-technical stakeholders.
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Education: Master’s or Ph.D. in Computer Science, Mathematics, Financial Engineering, or a related quantitative field from a reputed institution.
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Overall 9-15+ years of IT experience
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Must have at least 5-8+ years of progressive experience in software engineering and quantitative development.
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Strong experience with data processing libraries such as Pandas, Polars, CuML, etc
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Familiarity with cloud big data platforms, particularly Databricks.
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Experience working with large datasets and building scalable data pipelines.
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Proficiency in Python and PySpark (must-have), with hands-on experience in R and Java.
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Domain Knowledge: Solid understanding of financial instruments including securities and derivatives, along with capital markets structure.
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Development Practices: Strong commitment to clean code, adaptive systems, and iterative design using TDD methodologies.
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Able to explain technical strategies and solutions to both technical and business audiences.
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Quick to learn new technologies and quantitative methods.
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Exposure to quantitative research and alpha modeling.
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Experience building risk engines or simulation frameworks.
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Familiarity with orchestration frameworks like Airflow or equivalent.
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Ability to work in a fast-paced, collaborative environment with minimal supervision.
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