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.

WHO WE LOOK FOR

Goldman Sachs is seeking a highly motivated AI Model Risk Associate to join our Model Risk Management (MRM) team in Birmingham. 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.

PREFERRED QUALIFICATIONS

• 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., NumPy, Pandas, TensorFlow, Pytorch).
• Understanding of statistical modelling and machine learning algorithms
• Excellent analytical, problem-solving, and communication skills.Demonstrated curiosity, ownership, and a willingness to work in a collaborative environment.

• 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.