Quantitative Modeling [Multiple Positions Available]

JPMorgan Chase & Co.•Jersey City, NJ

2d•Onsite

About The Position

Duties: Independently, collate data and evaluate the feasibility of using the data for developing models. Clean and enrich the data with various attributes so that it is fit for purpose. Lead model calibration on this data using statistical and machine learning tools. Document the models and the statistical robustness of these models. Build model prototype frameworks to forecast the macro-sensitivity of the models. Respond to model review and regulatory questions about model performance and applicability. Monitor performance of the models once it reviewed. Overlay models that fail performance metrics. Perform risk impact studies for new deals being originated by the desk. Create explanation of the model credit costs associated with loans on the balance sheet. Lead discussions with business leads and risk managers to develop new model frameworks for more exotic financial instruments like SRTs and CDOs. Simultaneously work on multiple projects and mentor junior members in the team. Evaluating talent for new positions and matching new hires skills with model projects.

Requirements

Master's degree in Quantitative and Computational Finance, Engineering, Statistics, Physics, Chemistry and Mathematics, or related field of study plus 2 years (24 months) of experience in the job offered or as Quantitative Modeling Researcher, Credit Risk Quantitative Research, Market Liquidity Risk Management (MLRM), or related occupation.
Developing models for pricing fixed income products including loans, bonds, credit fault swaps, credit default obligations, and synthetic risk transfers
Construction of risky pricing curves for a debt issuer using risk neutral mathematical models for fixed income products
Risk neutral pricing
Experience with change of numeraire to replicate exotic product pricing in terms of other vanilla fixed income products
Using quantitative research methods, including numerical techniques such as Markov chain, Monte Carlo Simulations, or other optimization techniques for solving non-linear equations, for credit ratings, collateral, and the debt structure of an issuer of debt
Blending machine learning techniques such as clustering, tree models and random forests with econometric modeling to build time series and panel data models for model development and calibration
Writing technical model documentation
Object-oriented programming in Python
Using Pandas and Numpy to manipulate datasets, quantify the distribution of underlying data, and develop robust models

Responsibilities

Collate data and evaluate the feasibility of using the data for developing models
Clean and enrich the data with various attributes
Lead model calibration on data using statistical and machine learning tools
Document the models and the statistical robustness of these models
Build model prototype frameworks to forecast the macro-sensitivity of the models
Respond to model review and regulatory questions about model performance and applicability
Monitor performance of the models
Overlay models that fail performance metrics
Perform risk impact studies for new deals being originated by the desk
Create explanation of the model credit costs associated with loans on the balance sheet
Lead discussions with business leads and risk managers to develop new model frameworks for more exotic financial instruments like SRTs and CDOs
Simultaneously work on multiple projects and mentor junior members in the team
Evaluating talent for new positions and matching new hires skills with model projects