Quantitative Trading and Research - SPG

Finance, Investment Banking Description This role is part of a high-performing quantitative modeling group within the Securitized Products Group (SPG) Quantitative Trading & Research (QTR), focusing on Residential Mortgage-Backed Securities (RMBS) and related structured products modeling. The team is responsible for developing and maintaining agency and non‑agency RMBS models and analytical tools used in CIB trading and research. Our prepayment and default models are also critical to other businesses within the firm for RMBS and mortgage loan valuation and risk management. Additionally, our models are used by external clients through OASis and BondStudio. This is a VP‑level position and will be part of the non‑agency RMBS modeling team, supporting the CIB SPG trading desks with modeling, valuation, market‑making activities, and risk assessment. One of your key responsibilities will be driving the modernization of credit modeling by utilizing machine learning and GenAI throughout the full model development lifecycle, including data processing, data exploration, model calibration, model performance monitoring, and analytical tools/reporting. You will collaborate with business, technology, market risk, and various other teams to support new model development or enhancements to existing models, deepen understanding of model behavior and trading insights, maintain proper model infrastructure and usage, and provide expert guidance and training to model users and clients. Responsibilities Develop and support advanced financial models for RMBS, enabling business portfolio management, trading, hedging, and risk assessment. Conduct model back‑testing, performance tracking, and provide business insights to inform portfolio management and trading strategies. Perform large‑scale data queries, processing, and machine learning (ML) analysis for RMBS prepayment and credit modeling using high‑quality calibration data. Build and optimize robust platforms for large‑scale data analysis to support various modeling initiatives. Develop new models and analytical tools, and implement them within our advanced, high‑performing mortgage loan/bond pricing and analytics framework. Design and implement analytical tools to monitor model performance and market conditions in Residential Mortgage‑Backed Securities (RMBS), enhancing business decision‑making processes. Oversee the maintenance and enhancement of existing infrastructure used for valuation and hedging of financial transactions. Provide support to internal and external clients regarding model usage, addressing inquiries, and facilitating training as needed. Collaborate closely with risk and model review groups to ensure proper model usage, conduct model reviews, and implement effective risk controls. Qualifications 3+ years of relevant experience working in a data‑driven research environment. Strong quantitative educational background: Master’s or PhD degree in a quantitative field. Advanced modeling skills in developing machine learning (ML), statistical, or econometric models, preferably with applications in financial fields. Strong programming skills in Python (C++ is a plus); proficiency in statistical Python packages such as NumPy, Pandas, and stats packages (StatsModels, scikit‑learn, SciPy, etc.) for data manipulation and statistical analysis. Experience working with large‑scale databases (e.g., PostgreSQL, Redshift) for machine learning analysis and modeling is desirable. Experience in data analysis focused on mortgage and loan performance datasets, specifically analyzing prepayment and credit historical data at the loan or facility level, is desirable. #J-18808-Ljbffr Qishicpc