Quant Analytics [Multiple Positions Available]

DESCRIPTION:

Duties: Develop, implement, and enhance the end-to-end model forecast processes.

Develop models to forecast the performance of credit card loans that are used for stress testing and business planning.

Analyze, prepare, and reconcile accounting with technical data and prepare reports.

Devise ways to streamline and minimize cycle times through automation and process improvements.

Support financial planning work streams, including budget, CECL, and stress testing by managing business strategic inputs and assumptions used in the forecasting model, as well as by performing volume and revenue analysis.

Conduct ad hoc analysis of the business and business drivers.

Develop recommendations and maintain financial and reporting systems impacting business procedures and operations.

Ensure data integrity, accuracy, and timeliness of forecasts.

Own model technical documentation authorship by clearly explaining model methodologies and supporting analysis.

Present findings to senior management.

Participate in the budget process by developing quantitative approaches driving budgetary assumptions.

Implement and enhance end-to-end model management of the various forecasting models owned by the team.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Statistics, Finance, Mathematics, Economics, or related field of study plus 2 years of experience in the job offered or as Quant Analytics, Statistical Modeler Intern, or related occupation.

The employer will alternatively accept a Bachelor's degree in Statistics, Finance, Mathematics, Economics, or related field of study plus 5 years of experience in the job offered or as Quant Analytics, Statistical Modeler Intern, or related occupation.

Skills Required: This position requires one (1) year of experience with the following: Building budget and CCAR forecasting tools for card PPNR metrics using analytics platforms including Python, AWS Sagemaker, Databricks, and Alteryx; Developing statistical models for Budget and CCAR forecasting applying statistical forecasting methods including ARIMA, Holt-Winters, MSTL, PCA, regression models; Conducting budget and CCAR forecasting using machine learning techniques, including decision trees, regression analysis, and principal component analysis.

This position requires any amount of experience with the following: Building timeseries, regression, econometric, and machine learning models using Python; Performing data analysis and visualization in AWS using Python and PySpark; Performing data querying, analysis, and visualization using SQL in Snowflake, Databricks, and Teradata; Generating insights through data manipulation, data structuring, data design flow, and query optimization using SQL and Python; Designing and developing interactive Excel and PowerPoint reports using advanced functionalities including vlookup, index match, data analysis add-ons, and pivots; Analyzing the impact of macroeconomic factors on the card business by perfo...