Enterprise Portfolio Researcher
at Millennium
Posted a month ago
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- Compensation
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- City
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Seeking a motivated Quantitative Risk Modeler with 1-3 years of experience to develop and maintain multi-asset class analytics frameworks supporting firmwide portfolio analytics and senior management decision-making. Responsibilities include building centralized performance evaluation, VaR and stress-testing methodologies, back-testing/model performance frameworks, and visualization tools. The role involves ownership of quantitative frameworks for measuring and reporting multi-asset analytics, PM performance measurement, capital allocation models, and coordinating deployment with Technology teams. Strong programming (Python) and statistical modeling skills are required along with good communication for senior management presentations.
Principal Responsibilities:
- Development of multi-asset class analytics across all MLP strategies, supporting the Office of the CIO across Enterprise-wide initiatives
- This includes working on the centralized performance evaluation framework at MLP, improvements on VaR and Stress methodologies, as well as implementing centralized back-testing and model performance frameworks
- Contributions to the development of multi-asset class content generation, as well as centralized visualization tools for the platform used by senior management.
- Ownership in developing a quantitative framework for identifying, measuring, managing, and reporting multi-asset class analytics across the platform.
- PM performance measurement and analytics to help inform management decisions.
- Ownership of a multi-asset class stress-testing framework, including insights into key risk drivers to action management decisions.
- Capital utilization and allocation models across portfolio manager teams. Cost of liquidation measurement and management, as well as associated returns relative to constrained resources.
- Post initial model development work, coordinate with relevant Technology departments to ensure changes are deployed into to production
- The candidate should have a degree in a quantitative field such as statistics, mathematics, computer science or financial engineering
- Strong programming skills, prior experience with Python (Polars and/or Pandas). Proficiency in at least a compiled and statically typed language is a plus
- Knowledge of mathematical and statistical analytics tools: estimation of linear models, dimensionality reduction techniques e.g. Equity Factor Models, Principal Component Analysis, and performance analytics (e.g., Sharpe ratios, drawdowns).
- Sense of responsibility and integrity. Intellectual curiosity and entrepreneurial mindset. Willingness to work and have fun in the process.
- Good presentation and communication skills, experience in either preparing or participating presentation for senior management-style meetings
