• Associate, CIMD4326443

    Location(s) US-NY-New York
    Job ID
    Schedule Type
    Full Time
    Investment Management
    Consumer and Investment Management Division
    Business Unit
    Loans Credit Risk
    Employment Type

    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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