Quantitative Investments Risk Associate (Hedge Funds)
Company Overview
Our client is a premier global alternative asset management firm with a highly respected investments platform. Their established Investments Risk team has built out the firm's core proprietary risk and attribution models, optimization frameworks, alpha analysis, and client reporting infrastructure. They are seeking an experienced professional to help shape investment and portfolio decisions across the firm's hedge fund activity.
Position Overview
In this role, you will use quantitative risk analysis—including alpha decomposition, stress testing, and optimization—to inform manager evaluation, position sizing, and asset allocation. You will build and apply the risk tools and analytics underlying this work, contributing to both internal risk oversight and external investor reporting.
This position offers a high degree of autonomy. While much of the work is well-defined, you will have the opportunity to identify problems worth solving on your own initiative, evolve existing infrastructure, and design roadmaps for unresolved challenges.
Schedule: Hybrid (3 days onsite: Tuesday, Wednesday, and Thursday).
Key Responsibilities
- Quantitative Risk Modeling: Build and maintain quantitative risk models and analytics (including factor and alpha decomposition, stress testing, and scenario analysis) working with large, structured datasets (SQL, data lakes) and using Python.
- Portfolio Construction & Analysis: Support manager evaluation and investment decisions by conducting portfolio risk monitoring and asset allocation analysis.
- Market Monitoring: Track and interpret risk exposures, factor trends, and capital market conditions relevant to specific hedge fund strategies.
- Stakeholder Collaboration: Partner with portfolio managers and the Investment Committee on portfolio construction decisions.
- Reporting & Presentation: Design and deliver polished risk and performance presentations for internal oversight and external investor transparency requests.
Requirements & Experience
- Education: Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, Mathematics, Statistics/Econometrics, Financial Engineering) or equivalent technical experience.
- Experience: 3 to 7 years of experience in investment analysis, risk management, or asset allocation, ideally with exposure to hedge fund or absolute return strategies.
- Technical Skills: Proficiency in Python and hands-on experience working with large structured datasets. Comfort using AI-assisted tools where appropriate is a plus.
- Communication: Outstanding written and verbal communication skills, with a proven ability to translate complex quantitative analysis into clear, actionable conclusions for investment committees and clients.
- Presentation: Strong skills in building and delivering highly polished materials in PowerPoint or other reporting formats.
- Initiative: A self-starter mentality with the judgment and ownership to operate effectively in environments where directions are not always fully specified.
Quantitative Investments Risk Associate (Hedge Funds)
Company Overview
Our client is a premier global alternative asset management firm with a highly respected investments platform. Their established Investments Risk team has built out the firm's core proprietary risk and attribution models, optimization frameworks, alpha analysis, and client reporting infrastructure. They are seeking an experienced professional to help shape investment and portfolio decisions across the firm's hedge fund activity.
Position Overview
In this role, you will use quantitative risk analysis—including alpha decomposition, stress testing, and optimization—to inform manager evaluation, position sizing, and asset allocation. You will build and apply the risk tools and analytics underlying this work, contributing to both internal risk oversight and external investor reporting.
This position offers a high degree of autonomy. While much of the work is well-defined, you will have the opportunity to identify problems worth solving on your own initiative, evolve existing infrastructure, and design roadmaps for unresolved challenges.
Schedule: Hybrid (3 days onsite: Tuesday, Wednesday, and Thursday).
Key Responsibilities
- Quantitative Risk Modeling: Build and maintain quantitative risk models and analytics (including factor and alpha decomposition, stress testing, and scenario analysis) working with large, structured datasets (SQL, data lakes) and using Python.
- Portfolio Construction & Analysis: Support manager evaluation and investment decisions by conducting portfolio risk monitoring and asset allocation analysis.
- Market Monitoring: Track and interpret risk exposures, factor trends, and capital market conditions relevant to specific hedge fund strategies.
- Stakeholder Collaboration: Partner with portfolio managers and the Investment Committee on portfolio construction decisions.
- Reporting & Presentation: Design and deliver polished risk and performance presentations for internal oversight and external investor transparency requests.
Requirements & Experience
- Education: Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, Mathematics, Statistics/Econometrics, Financial Engineering) or equivalent technical experience.
- Experience: 3 to 7 years of experience in investment analysis, risk management, or asset allocation, ideally with exposure to hedge fund or absolute return strategies.
- Technical Skills: Proficiency in Python and hands-on experience working with large structured datasets. Comfort using AI-assisted tools where appropriate is a plus.
- Communication: Outstanding written and verbal communication skills, with a proven ability to translate complex quantitative analysis into clear, actionable conclusions for investment committees and clients.
- Presentation: Strong skills in building and delivering highly polished materials in PowerPoint or other reporting formats.
- Initiative: A self-starter mentality with the judgment and ownership to operate effectively in environments where directions are not always fully specified.