Principal, Structured CRE/BPL Resi Desk Strat

About the role

The Principal-level Structured Commercial Real Estate/Business-Purpose Resi loan Desk Strat will join Apollo's Global Investment Insights team in Los Angeles. This high-impact role involves building and institutionalizing cash flow modeling, deal structuring analytics, and risk assessment capabilities across Apollo’s structured CRE/BPL investment strategies.

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.
• 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.

Pay

$300,000