Principal, Structured CRE​/BPL Resi Desk Strat

Principal-Level Structured Commercial Real Estate/Business-Purpose Resi Loan Desk Strat

Apollo is seeking a Principal-level Structured Commercial Real Estate/Business-Purpose Resi loan Desk Strat to join its Global Investment Insights team in Los Angeles. This individual will be responsible for building and institutionalizing cash flow modeling, deal structuring analytics, and risk assessment capabilities across Apollo's structured CRE/BPL investment strategies, including Conduit CMBS, CRE CLO, Net Lease ABS, C-PACE, Residential Transitional Loans, Single Family Rental, Build-to-Rent, Land banking, and Agricultural Loans.

This is a high-impact role at the intersection of quantitative analytics and structured commercial and business-purpose residential real estate investing, embedded within a centralized investment capability that serves the full breadth of Apollo's credit platform. The successful candidate will partner directly with investment teams, portfolio risk, and senior leadership to deliver scalable, code-based modeling frameworks that support pricing, structuring, and risk management for pools of commercial mortgage loans across multiple securitization formats.

As a Principal within Global Investment Insights, this individual will serve as the domain expert for structured CRE/BPL resi, owning the end-to-end quantitative framework for modeling commercial mortgage loan pools and their securitized structures. The role demands deep technical fluency in CRE/BPL collateral analysis, waterfall modeling, and tranche-level risk assessment, combined with the ability to operate as a strategic partner to investment professionals and build durable, enterprise-grade analytics.

Consistent with Global Investment Insights' vision of embedding analytics and AI directly into investment workflows, the successful candidate will be expected to actively explore and integrate machine learning and AI techniques—such as automated collateral screening, property valuation models, and scenario generation—into the structured CRE/BPL analytical toolkit.

• Design, build, and maintain cash flow models for pools of commercial mortgage loans across Conduit CMBS, CRE CLO, Net Lease ABS, C-PACE securitization formats, and for business-purpose residential loans across Residential Transitional Loans, Single Family Rental, Build-to-Rent, Land banking strategies, and Agricultural loans.
• Develop standardized, code-based waterfall engines that model deal structures including credit enhancement, sequential and pro-rata pay tranches, reserve accounts, interest rate hedging, and loss allocation mechanics.
• Construct loan-level default, loss severity, and prepayment models calibrated to property type, geography, leverage, and borrower characteristics, supporting both base-case and stress scenario analysis.
• Build and maintain collateral performance frameworks that enable systematic surveillance of underlying CRE loan pools across the portfolio lifecycle.
• Partner with investment teams to provide quantitative analytics in support of new deal evaluation, pricing, and relative value assessment across structured CRE/business-purpose residential products.
• Develop scenario and sensitivity frameworks to assess the impact of macro variables (interest rates, cap rates, vacancy, rent growth) on deal economics and tranche-level returns.
• Support structuring decisions by modeling alternative capital structures, credit enhancement levels, and risk/return trade-offs for both primary issuance and secondary market opportunities.
• Contribute to the development of Global Investment Insights' firmwide quantitative infrastructure by integrating structured CRE models into Apollo's centralized analytics platform, supporting real-time portfolio risk reporting and regulatory capital stress analytics.
• Identify and implement opportunities to apply machine learning and AI techniques to structured CRE/BPL workflows—including property valuation, collateral screening, anomaly detection in loan pool performance, and scenario generation—ensuring applied AI is grounded in the analytical infrastructure that supports how the firm invests.
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