Quantitative Trading and Research - Securitized Products Group - Vice President

Testing Site - JPMC; New York (NY); On-site; USD 180,000 - 280,000; Full time

Job summary

Testing Site - JPMC in New York seeks a VP in Quantitative Research SPG to lead the development of advanced models for RMBS valuation, risk, and market making within the Global SPG.

You will collaborate with risk, model review, and client teams to ensure proper model usage, documentation, and training. The role emphasizes building scalable data analysis infrastructure and a new model library, with a focus on computational efficiency.

Qualifications

• PhD holder with strong math background and Python/C++ experience.
• 3+ years at VP level in quantitative research and modeling.
• Experience with RMBS/structured products data analysis.

Responsibilities

• Lead development and maintenance of advanced models for valuation and risk.
• Document models to comply with policy and risk controls.
• Design tools to monitor RMBS market conditions.
• Conduct data queries and prepayment/credit modeling.
• Build platform for large-scale data analysis.
• Oversee model usage and risk controls.

Skills

Python

C++

Monte Carlo

Logistic regression

PCA

Hazard rate models

Econometric modeling

Data analysis

Model risk awareness

Education

PhD in advanced maths

Tools

NumPy

Pandas

StatsModels

Shell scripting

Job description

This role sits within a high-performing quantitative modeling group focused on Residential Mortgage-Backed Securities (RMBS) and related structured products. The team is responsible for developing, maintaining, and enhancing advanced models and analytical tools that drive valuation, risk assessment, and market-making activities across the firm’s trading and risk management functions.

Job Summary:

As a Vice President in the Quantitative Research SPG team, you will play a pivotal role in supporting the Global Securitized Product Group (SPG) business. Your responsibilities will include leading the development, documentation, and enhancement of advanced quantitative models and analytical tools for SPG. You will collaborate with the business, risk, and model review teams to support proper model usage, maintain infrastructure, and provide expert guidance and training to users and clients.

Job Responsibilities:

• Lead the development and maintenance of advanced models for valuation, risk assessment, profit and loss (P&L) calculations, as well as algorithms for quoting and market making, utilizing sophisticated mathematical approaches.
• Ensure comprehensive documentation of all new models to comply with firm-wide model risk policies and procedures.
• Design and implement analytical tools to monitor market conditions in Residential Mortgage-Backed Securities (RMBS), enhancing decision-making processes.
• Conduct data queries and processing for RMBS prepayment and credit modeling, ensuring high-quality data analysis at the loan or facility level.
• Investigate and develop new techniques to improve mathematical and computational efficiency within modeling processes.
• Ensure appropriate model usage across a diverse range of business users and risk functions, providing guidance and training as needed.
• Build and optimize a robust platform for large-scale data analysis to support various modeling initiatives.
• Develop a new model library focused on achieving desired computational efficiency and usability.
• Oversee the maintenance and enhancement of existing infrastructure used for valuing and hedging financial transactions.
• Work closely with risk and model review groups to ensure proper model usage, conduct model reviews, and implement effective risk controls.
• Provide support to internal and external clients regarding their model usage, addressing inquiries and facilitating training as needed.

Required qualifications, capabilities, and skills:

• 3+ years of experience at the Vice President level.
• Proficient in Python and C++ for developing analytical tools and models.
• PhD in advanced maths
• Skilled in working within a Linux shell environment, utilizing shell scripting for automation and data processing.
• Extensive experience in data analysis focused on mortgage and loan performance datasets, specifically analyzing prepayment and credit historical data at the loan or facility level.
• Expertise in developing econometric models to assess financial performance and risk.
• Proficient in conducting Monte Carlo simulations for risk analysis and forecasting.
• Experienced in developing logistic regression models to predict binary outcomes based on historical data.
• Knowledgeable in developing factor models, including Principal Component Analysis (PCA) and hazard rate models.
• Proficient in using statistical Python packages such as NumX, Pandas, and StatsModels for data manipulation and statistical analysis.