Associate with Goldman Sachs Bank USA in New York, New York.
Work Schedule: 40 hours per week (9:00 a.m. to 6:00 p.m.)
Duties: Associate with Goldman Sachs Bank USA in New York, New York within the Loans Credit Risk business unit, responsible for: performing statistically driven credit underwriting and risk analyses using a variety of analytical techniques and data sources for the development of digital lending products across accept/decline, pricing and term optimization; analyzing credit bureau data to identify and derive attributes for credit risk decisions; building and optimizing existing customer management strategies to improve customer level profitability; determining optimal decision process in terms of validation of income and ability to pay to drive improved credit performance with the least incremental friction for customers; partnering with marketing, finance and operations teams to ensure appropriate execution of credit policy requirements; analyzing and evaluating potential vendors/data providers for incremental value in credit management strategies in a continuous improvement environment; staying up-to-date with applicable regulations and compliance requirements with possible interactions with internal and external regulators; and developing analytical reports and presentations for senior management, executive committees and regulatory exams.
Job Requirements: Master’s degree (U.S. or equivalent) in Mathematics, Statistics, or a related field. Three (3) years of experience in the job offered or in a related position. Prior experience must include (3) three years of experience with: working in risk management in a digital consumer or small business lending context or similar data driven industries like insurance; analyzing U.S. credit bureau data and risk scorecards; utilizing data analytical tools such as R (or Python) and SQL to identify credit behavior signals and make underwriting policy proposals; using Microsoft Office products, specifically Excel (including creation of pivot tables, logic functions, data tables and regression analysis), PowerPoint and Word; and developing comprehensive risk management reports using SQL or a similar database query language.
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