Risk, AI Model Validation, Vice President, New York

MODEL RISK MANAGEMENT (MRM)

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, Bangalore and Hyderabad. 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.

Who We Look For

Goldman Sachs is seeking a highly motivated AI Model Risk Vice President to join our Model Risk Management (MRM) team in New York. 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.

Responsibilities

• 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.

Preferred Qualifications

• A Ph.D. degree in Computer Science, Mathematics, Physics, Engineering, or a closely related quantitative field.
• Understanding of statistical modelling and machine learning algorithms
• 5 years of working experience in a quantitative field as a model developer or a data researcher.
• Preferable skillset: some experience in AI models
• Programming expertise in Python, including experience with relevant data science libraries (e.g., NumPy, Pandas, TensorFlow, Pytorch).
• Excellent analytical, problem-solving, and communication skills.
• Demonstrated curiosity, ownership, and a willingness to work in a collaborative environment.

ABOUT GOLDMAN SACHS

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.

We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.

We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html