VP, Quant Modeling Lead

DESCRIPTION:

Duties: Lead the design, development and implementation of advanced portfolio risk models.

Oversee the preparation of comprehensive presentations of econometric models.

Ensure precision and reliability in model result interpretation to business partners.

Conduct in-depth analysis of model performance and trends for strategic decision-making.

Lead team efforts with loss forecasting and business teams to address client issues in model construction.

Accurately translate regulatory requirements mandated for large financial institutions to design, modify and simplify model stress testing exercises.

Oversee testing, validation, and outcome analysis of models.

Identify model limitations, communicating findings to stakeholders.

Provide thought leadership to address complex business challenges.

QUALIFICATIONS:

Minimum education and experience required: PhD in Finance, Mathematics, Mathematical Finance, Economics, Statistics, or related quantitative field of study.

plus three (3) years of experience in the job offered or as Quantitative Researcher, Risk/Quantitative/Model Associate, Applied Economics Modeler/Researcher, Intern (Quantitative-related), Research Assistant, or related occupation.

The employer will alternatively accept a Master's degree in Finance, Mathematics, Mathematical Finance, Economics, Statistics, or related quantitative field of study plus six (6) years of experience in the job offered or as Quantitative Researcher, Risk/Quantitative/Model Associate, Applied Economics Modeler/Researcher, Intern (Quantitative-related), Research Assistant, or related occupation.

Skills Required: This position requires 3 years of experience with the following: working for a global financial institution working in credit risk modeling.

This position requires experience with the following skills: Credit risk modeling in consumer financial products; developing Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models in at least one of the following: CCAR, CECL, or BASEL; Using linear and logistic regression models for identifying relationships and predicting outcomes; model diagnostics to address multicollinearity, heteroscedasticity, and autocorrelation; panel data analysis with fixed effects, random effects, and mixed models; discrete choice models to predict credit events like default or delinquency; Data collection and integration from various sources, data cleaning, and preprocessing time series data, handling missing values and outliers; performing data normalization and transformation; conducting exploratory data analysis using histograms, scatter plots, and correlation matrices; Utilizing probability theory to assess the likelihood of credit events and defaults; applying multivariate statistical techniques for dimensionality reduction and identifying patterns in customer segmentation and risk factors; Analyzing macroeconomic indicators to understand their impact on credit risk; conducting stress ...