Quant Modeling Lead [Multiple Positions Available]

JPMorgan Chase & Co.•Jersey City, NJ

3h•Onsite

About The Position

Duties: Design and develop independent benchmark models for credit card models, including dataset preparation, model design, performance testing. Conduct comprehensive assessments on the benchmark credit model used for loss forecasting to ensure the robustness and reliability of the champion credit model. Conduct model validation on credit risk models that utilize machine learning techniques with explainability to ensure compliance with regulatory policy. Provide guidance on a model's appropriate usage and ensure that model users are aware of model strengths and limitation. Validate capital models and ensure they are compliant with Basel regulatory policy, including default definitions, methodology, and quantification criteria. Work with model developers to establish action plans and corresponding timelines for model risk issues. Conduct testing of LLM prompt engineering to facilitate automating certain governance processes. Assist with regulatory examinations, by working with internal team to deliver precise and accurate responses to inquiries. Assess qualitative adjustments to capture risks not reflected in the model output to comply with CECL policy. Review qualitative model (QM) requested by regulators by evaluating business assumption and quantitative techniques. Organize quarterly exit meetings and present a summary of the ongoing monitoring plan for credit risk models to senior management team. Coordinate governance activities, including performance monitoring and annual assessments, by collaborating with product teams and managing processes and deadlines. Oversee the management of model risk issues and limitations for credit risk models, ensuring they are accurately documented on the platform. Collaborate with the modeling team to design Ongoing Performance Monitoring (OPM) and Early Warning Analysis (EWA) for model performance in production. Collaborate with the teams to complete model review documentation, including evaluation of key elements of model risk, assessment of estimation diagnostics, assessment of implementation testing, outcome analysis, and ongoing performance monitoring.

Requirements

Master's degree in Quantitative and Computational Finance, Statistics, Data Analytics, Mathematics, or related quantitative field of study plus three (3) years of experience in the job offered or as Quant Modeling Lead, Quantitative Modeling/Management Associate, Model Risk Associate, or related occupation.
performing data manipulation, data structuring, and data design flow and query optimization using Python, R, and SQL programming languages
conducting benchmarking and statistical analysis using fundamental statistical learning algorithms, including linear regression, logistic regression, and clustering
optimizing XgBoost models via hyperparameter tuning
assessing XgBoost model performance using feature importance analysis and Shapley value computations
developing and validating credit risk models (including default models for CCAR and CECL) tailored for credit card, mortgage, or auto loan retail products.

Responsibilities

Design and develop independent benchmark models for credit card models, including dataset preparation, model design, performance testing.
Conduct comprehensive assessments on the benchmark credit model used for loss forecasting to ensure the robustness and reliability of the champion credit model.
Conduct model validation on credit risk models that utilize machine learning techniques with explainability to ensure compliance with regulatory policy.
Provide guidance on a model's appropriate usage and ensure that model users are aware of model strengths and limitation.
Validate capital models and ensure they are compliant with Basel regulatory policy, including default definitions, methodology, and quantification criteria.
Work with model developers to establish action plans and corresponding timelines for model risk issues.
Conduct testing of LLM prompt engineering to facilitate automating certain governance processes.
Assist with regulatory examinations, by working with internal team to deliver precise and accurate responses to inquiries.
Assess qualitative adjustments to capture risks not reflected in the model output to comply with CECL policy.
Review qualitative model (QM) requested by regulators by evaluating business assumption and quantitative techniques.
Organize quarterly exit meetings and present a summary of the ongoing monitoring plan for credit risk models to senior management team.
Coordinate governance activities, including performance monitoring and annual assessments, by collaborating with product teams and managing processes and deadlines.
Oversee the management of model risk issues and limitations for credit risk models, ensuring they are accurately documented on the platform.
Collaborate with the modeling team to design Ongoing Performance Monitoring (OPM) and Early Warning Analysis (EWA) for model performance in production.
Collaborate with the teams to complete model review documentation, including evaluation of key elements of model risk, assessment of estimation diagnostics, assessment of implementation testing, outcome analysis, and ongoing performance monitoring.