Quant Modelling - Vice President

As part of Risk Management and Compliance, you will play a crucial role in keeping the company strong and resilient by anticipating and addressing new and emerging risks. Your expert judgement will be key in solving real-world challenges that impact the company, customers, and communities. The culture in Risk Management and Compliance encourages thinking outside the box, challenging the status quo, and striving to be best-in-class. Key Responsibilities: - Set standards for robust model development practices and enhance them as needed to meet evolving industry standards - Evaluate adherence to development standards including soundness of model design, reasonableness of assumptions, reliability of inputs, completeness of testing, correctness of implementation, and suitability of performance metrics - Identify weaknesses, limitations, and emerging risks through independent testing, building of benchmark models, and ongoing monitoring activities - Communicate risk assessments and findings to stakeholders, and document in high quality technical reports - Assist the firm in maintaining (i) appropriateness of ongoing model usage, and (ii) the level of aggregate model risk within risk appetite Qualifications Required: - A Ph.D. or Masters degree in a Data Science oriented field such as Data Science, Computer Science, or Statistics - 7+ years of experience in a quantitative modeling role, with hands-on experience in building/testing Machine Learning models - Domain expertise in Data Science, Machine Learning, and Artificial Intelligence, with knowledge in database interfacing and analysis of large data sets as a plus - Strong understanding of Machine Learning / Data Science theory, techniques, and tools including Transformers, Large Language Models, NLP, GANs, Deep Learning, OCR, XGBoost, and Reinforcement Learning - Proven managerial experience or demonstrated leadership abilities in quantitative teams - Proficiency in Python programming, with experience in Python machine learning libraries including NumPy, SciPy, Scikit-learn, Pandas, TensorFlow, Keras, and PyTorch - Understanding of the machine learning lifecycle from feature engineering to feedback loop - Strong communication skills both verbally and in writing, with the ability to interface with other functional areas in the firm on model-related issues and write high quality technical reports - Risk and control mindset with the ability to ask incisive questions, assess materiality of model issues, and escalate issues appropriately,