Vice President, Model Risk Management I

94_30357156

Obowiązki

• Perform validation of AI/ML models (primary focus): machine learning and artificial intelligence models, including applications of Large Language Models:
  • Review model architecture, feature engineering, hyperparameter tuning, and performance metrics.
  • Design and execute tests for conceptual soundness, robustness, bias, and real-time performance monitoring via shadow frameworks.
  • Assess AI explain ability, fairness, and governance controls.
• Provide ad-hoc review and validation of traditional quantitative models (econometric, statistical, pricing) as needed.
• Identify model-specific risks; propose, implement, and document controls to mitigate those risks.
• Collaborate with developers to challenge assumptions, refine methodologies, and enhance model lifecycles.
• Conduct independent research on emerging AI/ML techniques and translate insights into validation best practices.
• Prepare clear, concise validation reports and present findings to senior stakeholders. 

Wymagania

• Review model architecture, feature engineering, hyperparameter tuning, and performance metrics.
• Design and execute tests for conceptual soundness, robustness, bias, and real-time performance monitoring via shadow frameworks.
• Assess AI explain ability, fairness, and governance controls.

Oferujemy

• Master’s degree or PhD in a quantitative discipline (Engineering, Mathematics, Physics, Statistics, Econometrics, Data Science).
• 1–2 years’ experience post-Master’s (PhD holders with relevant research may qualify).
• Strong theoretical foundation in ML/AI techniques (supervised, unsupervised, deep learning).
• Hands-on experience with ML/AI frameworks and libraries (TensorFlow, PyTorch, scikit-learn, XGBoost, etc.)
• Programming proficiency in Python or R (MATLAB or similar acceptable).
• Excellent communication and presentation skills; ability to explain complex concepts to non-technical stakeholders.
• Keen interest in financial engineering, market-product modelling, econometrics, data science, or AI. 
  
   

Źródło: BNY/Praca