Enterprise Risk Modeling – Cross Asset Quant
About The Position
This role focuses on the development of cross-asset analytics for all MLP strategies, providing support to the Office of the CIO and Business Management for firm-wide initiatives. The position involves leveraging a multi-asset class risk and pricing analytics framework to generate insights from extensive datasets. Key contributions include developing multi-asset class content and centralized visualization tools for the platform. The role also requires specifying statistical modeling techniques for risk insights, including Value-At-Risk, Stress Testing, and Performance Analytics methodologies, as well as applying machine learning techniques to financial data. A crucial part of the role involves coordinating with Technology departments to deploy models into production after their initial development.
Requirements
- Degree in a quantitative major: Mathematics, Engineering, Statistics
- Professional experience of 2+ years in a quantitative role in a financial organization
- Sense of responsibility and integrity.
- Intellectual curiosity and entrepreneurial mindset
- Ability to work in a fast-paced environment and engage with senior management.
- Good presentation and communication skills, experience in either preparing or participating in presentations for senior management meetings.
Nice To Haves
- Knowledge of mathematical and statistical analytics tools: Estimation of linear models, dimensionality reduction techniques e.g. Equity Factor Models, Principal Component Analysis, etc.
Responsibilities
- Development of cross-asset analytics across all MLP strategies, supporting the Office of the CIO and Business Management across Firm-wide initiatives
- Leverage multi-asset class risk and pricing analytics framework to develop insights using rich datasets.
- Contributions to the development of multi-asset class content generation, as well as centralized visualization tools for the platform
- Specification of statistical modeling techniques to generate risk insights. Development of classical Value-At-Risk, Stress Testing, Performance Analytics methodologies, as well as the application of machine learning techniques (e.g. Gaussian Mixture Models, Hidden Markov Models, etc.) to financial data sets
- Post initial model development work, coordinate with relevant Technology departments to ensure changes are deployed into to production
Benefits
- Discretionary performance bonus
- Comprehensive benefits package
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What This Job Offers
Job Type
Full-time
Career Level
Principal
