Risk-Dallas-Associate-Model Risk

Goldman Sachs•Dallas, TX

About The Position

Goldman Sachs is seeking an AI Model Risk Associate to join its Model Risk Management (MRM) team in Dallas. The role involves validating the firm’s AI models to assess their accuracy, reliability, and compliance with regulatory requirements. The associate will work with AI researchers, model developers, and other stakeholders to understand model methodologies, assess risks, and implement validation plans and benchmarking models. The MRM group provides independent oversight of model risk across the firm and supports compliance with firmwide model-control policies and related standards. Its work includes reviewing models for validity, theoretical consistency, implementation accuracy, and risks associated with model choice. The position offers exposure to AI applications across a global financial institution and contributes to the firm’s risk management framework.

Requirements

  • 2–5 years of experience in AI/ML model validation or research, including modern AI techniques, machine learning research, quantitative research, or related fields.
  • Bachelor’s, master’s, 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 such as NumPy, Pandas, TensorFlow, or PyTorch.
  • Understanding of statistical modeling and machine learning algorithms.
  • Excellent analytical, problem-solving, and communication skills.
  • Demonstrated curiosity, ownership, and willingness to work in a collaborative environment.

Responsibilities

  • Independently validate the performance, accuracy, and reliability of the firm’s AI models, including their explainability, design, and algorithmic robustness.
  • Develop and implement validation methodologies and benchmark models suited to the characteristics of AI models.
  • Test and analyze model outputs, identifying and documenting potential risks and limitations.
  • Collaborate with model developers and business stakeholders to address identified issues and improve model performance.
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