Principal, Consumer Finance Investment Strategy

About the role

The role spans the full breadth of consumer asset classes, including Auto ABS, Credit Card ABS, Student Loan ABS, Personal Loan ABS, consumer whole loans, marketplace lending, and buy-now-pay-later (BNPL) platforms. This is a high-impact role at the intersection of quantitative analytics and consumer credit investing, embedded within a centralized investment capability that serves Apollo’s broader credit platform.

Primary Responsibilities

• Collateral Credit, Prepayment & Loss Modeling

• Construct and maintain loan-level and cohort-level default, loss severity, prepayment, and delinquency roll-rate models calibrated to borrower characteristics (FICO, DTI, LTV, vintage, geography) and macroeconomic variables (unemployment, interest rates, HPI).
• Develop scenario and stress testing frameworks to assess the sensitivity of collateral performance and tranche economics to adverse credit and macro environments.
• Build collateral surveillance tools that enable systematic monitoring of pool performance trends—including delinquency migration, cumulative net loss curves, and prepayment speeds—across both seasoned portfolios and new originations.
• Develop standardized tools to leverage internal, loan-level collateral credit models to analyze public ABS deals.
• Analyze emerging consumer credit segments (marketplace lending, BNPL, fintech-originated loans) and develop bespoke modeling approaches that capture their unique risk profiles, including limited performance histories and non-traditional underwriting criteria.

• Cash Flow & Structure Modeling

• Design, build, and maintain cash flow models for consumer finance transactions across Auto ABS, Credit Card ABS, Student Loan ABS, Personal Loan ABS, marketplace lending securitizations, and BNPL structures.
• Develop standardized, code-based waterfall engines that model deal structures including credit enhancement, excess spread mechanics, early amortization triggers, reserve accounts, and tranche-level loss allocation.
• Build flexible modeling frameworks that accommodate both term (amortizing) and revolving master trust structures, reflecting the distinct cash flow dynamics of each.
• Support structuring decisions by modeling alternative capital structures, credit enhancement levels, and risk/return trade-offs for both primary issuance and secondary market opportunities.

• Investment Support & Platform

• Partner with investment teams to provide quantitative analytics in support of new deal evaluation, pricing, and relative value assessment across consumer finance products—both securitized and whole loan.
• Contribute to the development of Global Investment Insights’ firmwide quantitative infrastructure by integrating consumer finance 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 consumer finance workflows—including borrower-level credit scoring and segmentation, alternative data integration, automated loan tape analysis, early warning systems for portfolio deterioration, and prepayment/default prediction—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 consumer finance capabilities remain best-in-class.
• Collaborate with technology and data teams to establish robust data pipelines, model governance, and version control practices for all consumer finance analytics.
• Provide mentorship and technical guidance to junior quantitative professionals supporting the consumer finance effort.

Qualifications & Experience

• Significant experience in consumer credit analytics, securitized products, or quantitative consumer finance, with deep domain expertise across one or more of: Auto ABS, Credit Card ABS, Student Loan ABS, Personal Loan ABS, consumer whole loans, marketplace lending, and/or BNPL.
• Demonstrated ability to build production-quality cash flow models for consumer finance transactions, including collateral performance modeling (default, prepayment, loss severity) and deal waterfall engines.
• Strong understanding of consumer credit fundamentals: borrower-level underwriting metrics, credit scoring methodologies, delinquency migration, recovery dynamics, and vintage analysis.
• Proficiency in programming languages and quantitative tools commonly used in structured finance and consumer credit modeling (e.g., Python, SQL, R, or equivalent).
• Familiarity with industry-standard structured finance cash flow modeling platforms (e.g., Intex, dv01, etc.) and consumer credit 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, deep learning for time series forecasting, alternative data analysis) to consumer credit or structured finance problems is a strong differentiator.
• A collaborative, “roll up your sleeves” mentality with a commitment to building scalable, institutional-grade analytics.
• Ability to program in C# and/or C++ to support model implementation.