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 testing and scenario analysis to evaluate the impact of adverse economic conditions on credit risk; Coding in SAS; conducting statistical analysis and generating reports using PROC MEANS and PROC LOGISTIC; developing and validating credit risk models; writing and debugging SAS macros; using PROC SQL for querying relational databases; Performing data analysis in Python using pandas and NumPy; conducting statistical analysis with SciPy and statsmodels; creating visualizations with matplotlib, seaborn, and Plotly; using Jupyter notebooks for interactive coding; Modeling and simulation methods including Monte Carlo simulation; conducting model validation, backtesting, and developing credit scoring; Multinomial, and spline regression; univariate, bivariate, binning, and clustering analysis; developing decision tree models using CART, selecting and engineering relevant features, and tuning hyperparameters; developing ARIMA models; using seasonal decomposition techniques, GARCH models for volatility forecasting, and Vector Autoregression (VAR) models to capture interdependencies between variables; understanding cointegration and correlation between time series variables; Using UNIX and Linux Shell Scripting to automate tasks, manage data, and integrate tools and processes; writing and debugging shell scripts; file and directory management; integrating shell scripts with SAS; managing user accounts and permissions; Connecting to Oracle and Teradata and using SQL for querying relational databases; using cloud platforms like AWS to perform computation; Using documentation and version control systems to prepare technical model documentation for transparency and effective communication with stakeholders; documenting the model development process; and ensuring documentation meets regulatory requirements under Basel III, CCAR, and CECL.
Job Location: 1111 Polaris Parkway, Columbus, OH 43240.
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, 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.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans