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, Landbanking, 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.

The Role

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.

Primary Responsibilities

• 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, Landbanking 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.
• Benchmark and adopt leading modeling practices and technologies from peer institutions, ensuring Apollo's structured CRE/BPL capabilities remain best-in-class.
• Collaborate with technology and data teams to establish robust data pipelines, model governance, and version control practices for all structured CRE/BPL analytics.
• Provide mentorship and technical guidance to junior quantitative professionals supporting the structured CRE/BPL effort.

Qualifications & Experience

• Significant experience in structured credit, securitized products, or quantitative CRE or business-purpose residential analytics, with deep domain expertise across one or more of: Conduit CMBS, CRE CLO, Net Lease ABS, C-PACE, SFR/BTR, Residential Transitional Loans, Landbanking and/or Agricultural Finance.
• Demonstrated ability to build production-quality cash flow models for securitized CRE/business-purpose residential transactions, including loan-level collateral modeling and deal waterfall engines.
• Strong understanding of CRE and business-purpose residential fundamentals: property-level underwriting, capitalization rates, net operating income, debt service coverage, and loan-to-value dynamics.
• Proficiency in programming languages and quantitative tools commonly used in structured finance modeling (e.g., Python, SQL, MATLAB, C# or equivalent).
• Familiarity with industry-standard structured finance cash flow modeling platforms and CRE/resi data providers is expected.
• Experience with securitization deal structures, credit enhancement mechanics, rating agency methodologies, and regulatory capital frameworks (Basel III / SCR) is strongly preferred.
• Excellent communication skills and the ability to translate complex quantitative concepts into actionable investment insights for senior stakeholders.
• Advanced degree in a quantitative discipline (finance, mathematics, statistics, engineering, computer science, or related field) preferred.
• Genuine conviction in the application of AI and machine learning to investment workflows. Experience applying ML techniques (e.g., gradient-boosted models, NLP for document extraction, neural networks for time series) to structured finance or real estate problems is a strong differentiator.
• A collaborative, "roll up your sleeves" mentality with a commitment to building scalable, institutional-grade analytics.