Model Risk, NY, Vice President Vice President

About the Team

Goldman Sachs's Model Risk Management (MRM) team plays a critical role in ensuring the safety and soundness of the firm's models. MRM validates a diverse set of models, including AI and machine learning models, used within the firm. This role offers the opportunity to significantly contributions to the firm's overall model risk management framework and AI.

The Model Risk Management (MRM) group is a multidisciplinary group of quantitative experts at Goldman Sachs with presence in New York, Dallas, London, Birmingham, Warsaw, Hong Kong, and Bangalore. The MRM group is responsible for independent oversight of Model Risk at the firm, ensuring compliance with Firmwide Policy on Model Control and related standards, including documentation to evidence effective challenge over the Model development, implementation, and usage of Models.

The group’s primary mandate is to manage risk that arises from models used in the firm through its range of businesses– from models used for derivatives valuation to models used for risk management, liquidity and capital computations. In addition to independently reviewing these classes of models for their validity, theoretical consistency and implementation accuracy, the group is also responsible to assess the risk associated with model choice, e.g., exposure to choice of model in various contexts such as pricing exotic options or in calculating capital.

Goldman Sachs is seeking a highly motivated AI Model Risk Professional to join our Model Risk Management (MRM) team in NYC. You will play a crucial role in validating the firm's AI models, ensuring their accuracy, reliability, and compliance with regulatory requirements. This is a unique opportunity to gain exposure to a wide range of AI applications within a leading global financial institution and contribute directly to the firm's risk management framework.

You will work closely with AI researchers, AI model developers, and other stakeholders to understand model methodologies, assess model risks, and implement validation plans and benchmarking models.

• Independently validate the performance, accuracy, and reliability of AI models used within Goldman Sachs, focusing on aspects such as accuracy, explainability, model design, and algorithmic robustness.
• Develop and implement validation methodologies and benchmark models tailored to the specific characteristics of AI models.
• Conduct thorough testing and analysis of model outputs, identifying and documenting potential risks and limitations.
• Collaborate with model developers and business stakeholders to address identified issues and improve model performance.

• A Bachelor, Master or Ph.D. degree in Computer Science, Mathematics, Physics, Engineering, or a closely related quantitative field.
• Programming expertise in Python, including experience with relevant data science libraries (e.g., Num Py, Pandas, Tensor Flow, Pytorch).
• Relevant experience working with statistical modelling, machine learning algorithms or natural language processing.
• Excellent analytical, problem-solving, and communication skills.
• Demonstrated curiosity, ownership, and a willingness to work in a collaborative environment.

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Goldman Sachs is an equal opportunity employer and does not…