Job summary: Financial Institutions routinely use models for a broad range of activities including analyzing business strategies, informing business decisions, identifying and measuring risk, valuing exposures or instruments, hedging derivative positions, conducting stress testing, assessing capital adequacy, managing clients assets, informing investment process, measuring compliance with internal limits, maintaining the formal control apparatus of the bank, meeting financial or regulatory reporting requirements and issuing public disclosures. Model Risk arises from the potential adverse consequences of making decisions based on incorrect or misused model outputs and reports, leading to financial loss, poor business decision making, or reputational damage. As part of the firm’s model risk management function, Model Risk Governance and Review Group (MRGR) is charged with developing model risk policy and control procedures, performing model validation activities, providing guidance on a model’s appropriate usage in the business context, evaluating ongoing model performance testing, and ensuring that model users are aware of the model strengths and limitations. Model manager roles within MRGR provide attractive career paths for model development and model validation quants in a dynamic setting working closely with Model Developers, Model Users, Risk and Finance professionals, where they act as key stakeholders on day-to-day model-related risk management decisions as well as conduct independent model validation of new and existing models. Core responsibilities: The successful candidate will be a member of the MRGR Group in Bengaluru covering all Data Science across all ex-trading applications of artificial intelligence and machine learning models across CIB: Engage in new model validation activities for all Data Science models in the coverage area - evaluate conceptual soundness of model specification; reasonableness of assumptions and reliability of inputs; fit for purpose; completeness of testing performed to support the correctness of the implementation; robustness of numerical aspects; suitability and comprehensiveness of performance metrics and risk measures associated with use of model. Conduct independent testing Perform additional model review activities ranging from proposed enhancements to existing models, extensions to scope of existing models. Liaise with Model Developers, Model Users, Risk and Finance professionals to provide oversight of and guidance on appropriate usage, controls around model restrictions & limitations, and findings for ongoing performance assessment & testing Maintain model risk control apparatus of the bank for the coverage area & serve as first point of contact Keep up with the latest developments in coverage area in terms of products, markets, models, risk management practices and industry standards Essential skills, experience, and qualifications : A Ph.D. or Master’s degree in a Data Science oriented field such as Data Science, Computer Science or Statistics, is required. Prior experience in following backgrounds (2-4+ years): Data Science, Quantitative Model Development, Model Validation or Technology focused on Data Science including hands on experience with building/testing machine learning models 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 Understanding of the machine learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop is an asset Proficiency in Python programming. Python machine learning library and ecosystem experience: Numpy Scipy Scikit-learn Theano TensorFlow Keras PyTorch Pandas Excellent writing skills: previous experience in writing scientific text with the ability to describe evidence and present logical reasoning clearly. Strong communication skills and ability to interface with other functional areas in the bank on model-related issues Risk and control mindset: ability to ask incisive questions, converge on critical matters, assess materiality and escalate issues
