Quantitative Developer

Job Summary: We are seeking a highly experienced and adaptable Quantitative Developer to join our team in Chicago, IL. 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.

Key Responsibilities
Design and implement production-grade code that translates quantitative models into efficient and scalable solutions.
Work closely with Quantitative Research, Risk, and Equity Portfolio Management teams to support model development and risk analytics.
Contribute across the software development lifecycle including requirements analysis, coding, testing, and deployment.
Build solutions using a wide array of technologies including Python, PySpark, R, Java, and cloud-based big data platforms like Databricks.
Develop in both real-time and batch-oriented architectures.
Employ Test-Driven Development (TDD) to ensure code quality, scalability, and maintainability.
Continuously explore and integrate modern technologies and industry best practices into development processes.
Communicate complex quantitative and technical concepts effectively to non-technical stakeholders.

Required Qualifications
Education: Master s or Ph.D. in Computer Science, Mathematics, Financial Engineering, or a related quantitative field from a reputed institution.
Experience:
o Overall 12+ years of IT experience.
o Must have at least 5-8+ years of progressive experience in software engineering and quantitative development.
Technical Skills:
o Proficiency in Python and PySpark (must-have), with hands-on experience in R and Java.
o Strong experience with data processing libraries such as Pandas, Polars, CuML, etc.
o Familiarity with cloud big data platforms, particularly Databricks.
o Experience working with large datasets and building scalable data pipelines.
Domain Knowledge: Solid understanding of financial instruments including securities and derivatives, along with capital markets structure.
Development Practices: Strong commitment to clean code, adaptive systems, and iterative design using TDD methodologies.
Soft Skills:
o Quick to learn new technologies and quantitative methods.
o Able to explain technical strategies and solutions to both technical and business audiences.

Preferred Attributes
Exposure to quantitative research and alpha modeling.
Experience building risk engines or simulation frameworks.
Familiarity with orchestration frameworks like Airflow or equivalent.
Ability to work in a fast-paced, collaborative environment with minimal supervision.