Job Responsibilities

• Conduct independent model validation and governance activities to mitigate model risk across a diverse portfolio, including CIB Wholesale Payments, CIB Fraud, Marketing, and Operations models.

• Engage in model validation activities and evaluate adherence to development standards including conceptual soundness of 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 benchmarks, and ongoing monitoring activities
• Communicate risk assessments and findings to stakeholders, and document high quality technical reports
• Assess and determine whether tools and applications qualify as models under the firm's model risk framework, distinguishing between models, analytical tools, and non-model applications

• Liaise with Risk and Finance professionals to provide oversight and guidance on appropriate usage, controls around model restrictions and limitations, and findings for ongoing performance assessment and testing

• Design, build, and test LLM-based use cases to enhance MRGR processes and improve operational efficiency

Required qualifications, capabilities and skills

• Strong quantitative and analytical skills: The role requires a strong quantitative background based on a Master or PhD Degree in a quantitative discipline such as Math, Statistics, Economics, Finance, Engineering, or related fields

• 2 plus years of experience in model validation, model development, quantitative analysis, or a related analytical role in financial services or a similar industry. 
• Strong communication skills, with the ability to present complex concepts to both technical and non-technical audiences. Risk and control mindset: ability to ask incisive questions, assess materiality of model issues, and escalate issues appropriately
• Strong foundation in statistics, with hands-on experience applying statistics and machine learning techniques, including Regression, Boosted Trees, Neural Networks, Support Vector Machines (SVM), and Large Language Models such as BERT. 
• Proficiency in Python, with hands-on experience using libraries for data analysis, machine learning, and working with LLM frameworks and APIs
• Experience with Generative AI applications, including Large Language Models, prompt engineering, RAG architectures, and building agentic AI systems.