Job Description

About Goldman Sachs
The Goldman Sachs Group, Inc. is a bank holding company and a leading global investment banking, securities and investment management firm. We provide a wide range of services to a substantial and diversified client base that includes corporations, financial investors, governments, non-profit organizations and high net worth individuals. Founded in 1869, the firm is headquartered in New York and maintains offices in London, Tokyo, Hong Kong, Singapore, Dallas, Bengaluru, Mumbai and other major financial centers around the world.

Risk Engineering Departments | Risk Architecture Group
Risk Engineering ("RE"), which is part of the Risk Division, is a central part of the Goldman Sachs risk management framework, with primary responsibility to provide robust metrics, data-driven insights, and effective technologies for risk management. RE is staffed globally with offices including Dallas, New Jersey, New York, Salt Lake City, London, Warsaw, Bengaluru, Singapore, and Tokyo. The Risk Architecture ("RA") group in RE is a multidisciplinary group of quantitative experts focusing on market and credit risk models. The group is responsible for employing advanced data science and statistical techniques to identify risk and capital vulnerabilities due to inaccuracies in risk/capital measures or to risk sensitivities to changes in market/economic conditions given the firm's positional exposures.

RA's Credit Risk Model Performance Analysis team, which has regional offices in Dallas and London, employs advanced statistical and analysis techniques to monitor and assess the accuracy of the firm's credit risk models with a particular focus on counterparty credit risk ("CCR"):

• Performs detailed drill-down analyses to identify root causes of inaccurate measurements and works with the credit risk modeling team and other stakeholders to remediate any identified issues
• Develops advanced statistical tests and quantitative techniques to facilitate the identification of issues with the models' volatility and correlation backtesting performance at the risk-factor level and with the models' exposure-level backtesting performance at key points across the projection grid

The team works with groups across Risk and Risk Engineering to obtain regulatory credit risk model approvals for the capital calculation and to fulfill all regulatory backtesting requirements supporting the approvals.

JOB DUTIES:

• Design, implement, and enhance CCR model back-testing tools, capabilities, and analysis and reporting framework
• Utilize advanced mathematical, statistical, and quantitative skills to develop and enhance the approaches to identify market themes to which the firm's positions are most exposed
• Work with credit risk specialists and modelers to develop and uplift the credit risk analysis tools, capabilities, and reporting through time
• Communicate complex mathematical ideas with internal/external stakeholders such as risk managers, market making businesses, technology, and senior management. Create and maintain comprehensive technical documentation of the methodologies covering purpose, testing description, and empirical evidence

MINIMUM EDUCATION AND EXPERIENCE REQUIRED:

Bachelor's degree (U.S. or foreign equivalent) in Mathematics, Statistics, Physics, or a related quantitative field and one (1) year of experience in the job offered or a related role;

(Preferred):

Analyst-level position:

Master's degree (U.S. or foreign equivalent) in Mathematics, Statistics, Physics, or a related quantitative field and one (1) year of experience in the job offered or a related role.

Associate-level position:

PhD (U.S. or foreign equivalent) in Mathematics, Statistics, Physics, or a related quantitative field and three to four (3-4) years of experience in the job offered or a related role;

SPECIAL SKILLS AND/OR LICENSES REQUIRED:

• Advanced probability and statistical methods including stochastic processes
• Applications of quantitative methods, such as stochastic simulations and numerical methods
• Programing / implementation of quantitative analytical tools and capabilities, such as ones to estimate and decompose the conditional distribution over a number of positions and risk factors and other decomposition tools to help identify the sources of risk, the sensitivities to them, and the risk-return trade-offs
• Programming languages, such as Python, C, and Java
• Working with large data sets
• Writing technical documents for quantitative analytical methods
• Presenting and communicating highly technical quantitative methods, results, and analysis to diverse audiences