Quant Modeling [Multiple Positions Available]

DESCRIPTION:

Duties: Establish and maintain standards for the development of models used in Wholesale Credit loan loss forecasting and Obligor Grading and enhance standards in accordance with evolving industry practices and regulatory expectations. Evaluate the adherence of model development processes to established standards by assessing the soundness of model design, validity of assumptions, reliability of input data, thoroughness of testing and implementation, and the appropriateness of performance metrics for Wholesale Credit loan loss forecasting and Obligor Grading models. Perform model reviews by identifying weaknesses, limitations, and emerging risks through techniques including benchmarking, independent testing, and continuous monitoring activities. Prepare detailed technical documentation and reports outlining model risk assessments and communicate findings and recommendations to internal stakeholders and senior management. Support the organization in ensuring appropriate use of models on an ongoing basis and contribute to maintaining the overall model risk within the firm's risk appetite framework. Participate in internal and external audits, as well as regulatory examinations related to model risk governance and compliance.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Financial Mathematics, Statistics, Economics, Finance or related field of study plus 4 years of experience in the job offered or as Quant Modeling, Model Risk Program, Risk Consulting or related occupation. The employer will alternatively accept a PhD in Financial Mathematics, Statistics, Economics, Finance or related field of study plus 2 years of experience in the job offered or as Quant Modeling, Model Risk Program, Risk Consulting or related occupation.

Skills Required: This position requires experience with the following: Building bespoke credit risk models for wholesale credit portfolios including Probability of Default, Loss Given Default, and Expected Credit Loss (ECL) in using Python for Commercial & Industrial and Commercial Real Estate loans; Applying statistical and machine learning techniques including Linear & Logistic Regression, Time Series Modeling, Decision Trees, Gradient Boosting Machines, Markov Chains, and Monte Carlo Simulations; Incorporating economic factors and macroeconomic scenarios for loss forecasting, stress testing, and regulatory compliance under frameworks including Basel, Comprehensive Capital Analysis and Review, Risk- Weighted Assets, and Current Expected Credit Loss; Performing stressed loss modeling, PPNR forecasting, allowance for credit losses, discounted cash flow analysis, and portfolio-level credit loss estimation; Utilizing Python, SQL, and Excel to implement financial models, analyzing results, and generate actionable insights for risk management and regulatory reporting; conducting Linear and Logistic Regression, Time Series Modelling, Decision Trees, Gradient Boosting Machines, Markov Chains, and Monte Carlo Simulations using International Financial Reporting Standard 9 for wholesale credit portfolios; Performing independent validation in Python, SQL and Excel of credit risk models for Wholesale Credit portfolios covering conceptual soundness, data quality, model performance, to ensure compliance with regulatory requirements for US including Federal Reserve Board and Office of the Comptroller of the Currency and non-US including Monetary Authority of Singapore, Prudential Regulation Authority, and European Banking Authority regulators; Developing Merton-type structural models and financial based reduced form models using Python and SQL for commercial loan portfolios to assess obligor credit worthiness in accordance with the Internal Ratings based approach outlined in Basel III regulatory requirements; Applying regression analysis, autoregressive models, and macroeconomic overlays to estimate default probabilities and credit migrations; Applying classification machine learning techniques including gradient boosting and random forests to develop credit scoring frameworks for corporates loan portfolios using PySpark; Integrating physical and transition climate risk factors into stress testing frameworks for wholesale credit portfolios; Building and maintaining data pipelines for credit risk models, using PySpark, SQL, and Python to process and prepare financial datasets; Developing risk models, backtesting frameworks, and analytical tools using Python, R, SQL, PySpark, C++,and VBA; Writing audit-ready technical documentation for model development, validation reports, and regulatory exams including CCAR and CECL.  

Job Location: 545 Washington Blvd, Jersey City, NJ 07310.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set, and location. For those in eligible roles, discretionary incentive compensation which may be awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. In addition, please visit: https://careers.jpmorgan.com/us/en/about-us.

Full-Time. Salary:  $167,000 - $215,000 per year.