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, maintaining, and enhancing advanced models and analytical tools that drive valuation, risk assessment, and market-making activities across trading and risk management functions.
Job Summary:
As an Analyst/Associate in the Quantitative Trading & Research (QTR) team, you will primarily support Non-Agency RMBS modeling, collaborating with the Chase Home Lending Capital Markets team on Hold-For-Investment (HFI) residential mortgage portfolio management and Mark-to-Market (MtM) securitization trading desks. You will need to be experienced in calibration, development, and enhancement of advanced quantitative models and analytical tools/reports for business-as-usual (BAU) support and risk management. The role involves close collaboration with business, technology, market risk, and other teams to support new model development, model enhancement, infrastructure maintenance, and user training.
Job Responsibilities:
- Develop and support advanced financial models for RMBS business portfolio management, trading, hedging, and risk assessment.
- Conduct model back-testing, performance tracking, and provide business insights for portfolio management and trading activities.
- Execute 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 the advanced mortgage loan/bond pricing and analytics framework.
- Design and implement analytical tools to monitor model performance and market conditions in RMBS, enhancing business decision-making.
- Oversee maintenance and enhancement of existing infrastructure used for valuation and hedging financial transactions.
- Provide support to internal and external clients regarding model usage, address inquiries, and facilitate training as needed.
- Collaborate with risk and model review groups to ensure proper model usage, conduct model reviews, and implement effective risk controls.
Required Qualifications, Capabilities, and Skills:
- Master’s or PhD degree in a quantitative field (e.g., mathematics, statistics, engineering, computer science, finance).
- Strong modeling skills in developing advanced machine learning (ML), statistical, or econometric models, preferably with applications in financial fields.
- Proficient programming skills in Python (C++ is a plus), SQL, and shell scripting for developing analytical tools and models.
- Experience using statistical Python packages such as NumPy, Pandas, StatsModels, scikit-learn, and SciPy for data manipulation and statistical analysis.
Preferred Qualifications, Capabilities, and Skills:
- 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.