Quant Investment Strategist – Portfolio Management Associate (New York)

We are seeking a highly analytical Quant Investment Strategist / Portfolio Management Associate to join a top performing Credit team of a $100B asset manager. In this role, you will be the primary engine behind the modeling and analytics that drive our portfolio construction and asset allocation.Working directly with the Senior Portfolio Manager (PM), you will leverage advanced quantitative techniques to build and optimize credit portfolios, with a heavy focus on Structured Products (CLOs, ABS, RMBS/CMBS) and Private Credit (Direct Lending, Specialty Finance). You will bridge the gap between complex data architecture and real-world investment execution.Key ResponsibilitiesPortfolio Construction & OptimizationDevelop and maintain sophisticated models for Asset Allocation across various credit sub-sectors.Implement optimization frameworks (e.g., Mean-Variance, Black-Litterman, or Factor-based) tailored for illiquid and structured credit assets.Conduct What-if scenario analysis to determine the impact of new deal inclusions on portfolio yield, duration, and risk-weighted capital.Quantitative Modeling & AnalyticsBuild cash flow engines and valuation models for complex Structured Products.Develop proprietary risk models to capture non-linear risks inherent in private credit and leveraged structures.Create automated tools for performance attribution, identifying drivers of alpha within the credit book.Strategic Decision SupportPartner directly with the PM to provide data-driven insights for investment committee memos.Monitor market signals and spread movements to suggest tactical rebalancing opportunities.Synthesize large datasets of loan-level data to identify structural trends in the private credit landscape.RequiredAdvanced Degree (Masters or PhD): Required in a STEM field (Mathematics, Physics, Financial Engineering, Computer Science, or Statistics).Experience: 3–7 years of professional experience in a quantitative research or portfolio management environment.Domain Expertise: Demonstrated knowledge of Credit Markets. Experience with Structured Products (CLOs) or Private Credit is highly preferred.Programming: Expert-level proficiency in Python (NumPy, Pandas, Scikit-learn) or R.Database/SQL: Ability to manipulate and query large-scale financial datasets.Optimization: Deep understanding of stochastic calculus, linear algebra, and optimization libraries.Tools: Familiarity with credit analytical tools (e.g., Intex, Bloomberg, or Moody's Analytics) is a plus.