AI Model Risk Validator - Validation & Benchmarking

Goldman Sachs Group; Birmingham; Hybrid; GBP 60,000 - 90,000; Full time

Job summary

Goldman Sachs in Birmingham seeks an AI Model Risk Associate to join the Model Risk Management (MRM) team. You will validate AI models, assess risks, and contribute to validation plans and benchmarks in a collaborative, fast-paced environment.

Applicants should hold a Bachelor, Master, or PhD in a quantitative field and have Python expertise with data science libraries. Strong communication and analytical skills are essential.

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.

Responsibilities

• Independently validate the performance, accuracy, and reliability of AI models used within Goldman Sachs, focusing on accuracy, explainability, model design, and algorithmic robustness.
• Develop and implement validation methodologies and benchmark models tailored to 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.

Skills

Python

NumPy

Pandas

TensorFlow

PyTorch

Communication skills

Education

Bachelor/Master/PhD in Computer Science, Mathematics, Physics, Engineering

Job description

Goldman Sachs in Birmingham seeks an AI Model Risk Associate to join the Model Risk Management (MRM) team. You will validate AI models, assess risks, and contribute to validation plans and benchmarks in a collaborative, fast-paced environment.

Applicants should hold a Bachelor, Master, or PhD in a quantitative field and have Python expertise with data science libraries. Strong communication and analytical skills are essential.