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# Quantitative Developer - Equity Factor Model Risk Technology

- **Company:** MILLENNIUM
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Big Data, Information Engineering, Distributed Systems, Python (Programming Language), Data Driven Tests, Apache Spark, Model Validation, Build Management, Data Lakes, Data Management, Data Lakehouse, Data Pipelines
- **Published:** August 7, 2026
- **Apply:** https://us.experteer.com/career/view-jobs/quantitative-developer-equity-factor-model-risk-technology-new-york-ny-usa-58831329

## About the Role

_ portfolio research processes Tasks * 3+ years Python development in buy-side finance * Advanced knowledge of AWS or GCP * Experience with data lakehouse architecture is a plus * Experience with Spark and Trino/Spark compute; Delta Lake and/or Iceberg is a plus * Application of quantitative and statistical methods to data-driven analysis * Broad understanding of equity markets and portfolio construction * Strong communication skills and ability to work with risk management and trading teams * Detail-oriented, quick learner, and adaptable in a high-paced environment * Proven track record in challenging environments Key requirements * base salary * discretionary performance bonus * comprehensive benefits package

## Description

Experteer Overview You will strengthen Millennium's equity analytics platform by advancing internal factor risk models and supporting MSCI Barra risk models. The role focuses on building scalable, data-intensive distributed systems for historical and real-time portfolio analytics. You'll automate workflows, enhance data pipelines, and integrate new analytics models with risk management and trading teams. This position offers exposure to cutting-edge data engineering and quantitative analytics in a fast-paced environment with meaningful impact. Compensation / Benefits * Develop expertise in Barra and proprietary factor risk models * Design and build big data infrastructure for automated portfolio research * Identify and implement process improvements to automate manual tasks and scale data pipelines * Collaborate with portfolio research on integrating new analytics models into delivery platforms * Conduct extensive back-testing of risk factor models * Support risk management and equity portfolio research processes Tasks * 3+ years Python development in buy-side finance * Advanced knowledge of AWS or GCP * Experience with data lakehouse architecture is a plus * Experience with Spark and Trino/Spark compute; Delta Lake and/or Iceberg is a plus * Application of quantitative and statistical methods to data-driven analysis * Broad understanding of equity markets and portfolio construction * Strong communication skills and ability to work with risk management and trading teams * Detail-oriented, quick learner, and adaptable in a high-paced environment * Proven track record in challenging environments Key requirements * base salary * discretionary performance bonus * comprehensive benefits package


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