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About the Role
The Equity Factor Model team partners across portfolio risk research, equity risk modeling, and technology to advance risk and performance platforms supporting quantitative research and investment decision-making.
Responsibilities
- Serve as the primary liaison between quantitative research and software engineering teams, translating model specifications and investment strategies into clear, testable technical requirements.
- Manage the end-to-end requirements lifecycle for equity risk modeling and trading tools, from discovery and functional specifications through acceptance criteria and delivery.
- Design and oversee the integration of quantitative models and datasets into the firm’s trading and risk infrastructure.
- Analyze and prototype against market, portfolio, and model data to validate inputs and outputs, strengthen data integrity, and support the quantitative research pipeline.
- Lead end-user testing and feature validation to ensure delivered solutions reflect the mathematical and business intent of investment professionals and portfolio research teams.
- Create and maintain clear documentation for quantitative tools and data feeds, including functional specifications, model logic descriptions, and data dictionaries.
Requirements
- Advanced degree (PhD or Masters) in finance, financial engineering, mathematics, statistics, economics, physics, computer science, or another quantitative discipline.
- CFA is a plus.
- At least 2 years of professional work experience (post-PhD) as a business analyst or project manager within a hedge fund, asset management firm, or investment bank.
- 5+ years of professional work experience (post-Masters) as a business analyst or project manager within a hedge fund, asset management firm, or investment bank.
- At least 2 years of professional work experience working with software development lifecycles and Agile delivery methodologies.
- 5+ years of professional work experience working with software development lifecycles and Agile delivery methodologies (for Masters degree holders).
- Strong knowledge of asset pricing, quantitative factor risk modeling, portfolio optimization and construction, and core risk management concepts.
- Proficiency in Python and SQL for data analysis, validation, and prototyping.
- Experience partnering directly with portfolio researchers and software engineers to translate complex quantitative needs into actionable technical specifications.
- Excellent written and verbal communication skills, with the ability to explain mathematical concepts accurately to technical and non-technical stakeholders.
- Experience with equity factor risk models, performance measurement and attribution, data quality controls, reproducible pipelines, model governance, or market data technologies is preferred.
Skills
- Python
- SQL
- Agile delivery methodologies
- Quantitative factor risk modeling
- Portfolio optimization
- Portfolio construction
- Risk management
- Asset pricing
- Equity factor risk models
- Performance measurement
- Performance attribution
- Data quality controls
- Reproducible pipelines
- Model governance
- Market data technologies
Location
- New York City
Work Type
- Full-time
Experience Level
- 2+ years post-PhD
- 5+ years post-Masters
Education Level
- PhD or Masters in a quantitative discipline
- CFA
Salary/Compensations
- $175,000 to $250,000
Benefits
- Discretionary performance bonus
- Comprehensive benefits
About the Company
- Millennium is a global, diversified alternative investment firm, founded in 1989.
- Defined by evolution, innovation and focus, Millennium’s mission is to deliver results for our investors.
- Our people are empowered with both independence and support: the autonomy to pursue ideas with conviction and the backing of a global network committed to collaboration, disciplined risk management and continuous learning.
- With opportunities to deepen expertise and accelerate development, talent at Millennium is equipped to adapt, evolve and build lasting impact over time.
- Discover how transformative growth accelerates impact.