Quantitative Developer - Equity Factor Model Risk Technology

MILLENNIUM
Hope, United States of America
3 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate

Job location

Hope, United States of America

Tech stack

Amazon Web Services (AWS)
Big Data
Information Engineering
Distributed Systems
Python
Data Driven Tests
Spark
Model Validation
Build Management
Data Lake
Data Management
Data Lakehouse
Data Pipelines

Job 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

Requirements

_ 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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