Master's degree or PhD in a quantitative discipline such as Engineering, Mathematics, Physics, Statistics, Econometrics, or Data Science. 4-5 years' experience post-Master's degree (PhD holders with relevant research experience may qualify). Strong theoretical foundation in ML/AI techniques including supervised, unsupervised, and deep learning methods. Hands-on experience with ML/AI frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and XGBoost. Programming proficiency in Python or R (MATLAB or similar acceptable). Excellent communication and presentation skills with the ability to explain complex concepts to non-technical stakeholders. Keen interest in financial engineering, market-product modelling, econometrics, data science, or AI. Preferred qualifications include: Prior model-validation experience in ML/AI, Credit, Market, Treasury, or Pricing risk. Exposure to CCAR, CECL, or IFRS 9 regulatory frameworks. Proven track record applying advanced ML/AI methods such as NLP, computer vision, or reinforcement learning. Detail-oriented, results-driven, and highly productive in fast-paced teams. Previous experience in financial services and model governance.
