Thematic Risk Analytics Lead Analyst – Vice President

Introduction   An individual in Enterprise Risk Management plays a critical role in managing the bank's diverse risks to ensure financial stability and sustained growth. This involves the identification and management of enterprise-level and cross-cutting risks, designing and executing stress tests, managing climate risk, and protecting against reputational risk. This integral role within the bank ensures operations are within a defined risk appetite and contribute to the overall   objectives   of the bank.   This role is a unique and exciting opportunity to build the future of thematic risk using   cutting-edge   data science and AI.

Lead the design, development, and strategic deployment  of advanced AI and machine learning models to   identify , analyze, and   monitor   emerging thematic risks across global markets.   Drive the conception and implementation  of sophisticated Agentic AI systems for autonomous and proactive risk detection, analysis, and alerting.   Architect and oversee the management  of large-scale Knowledge Graphs to map and understand complex, interconnected risk ecosystems.   Leverage Retrieval-Augmented Generation (RAG) techniques to extract and synthesize actionable intelligence from vast unstructured and structured datasets.   Champion the development  of proof-of-concepts and rapidly prototype new AI-driven risk management tools and platforms, guiding their evolution to production.   Independently design and execute analysis of large-scale data populations aggregated from target platforms, processes, and product lines, consisting of structured and unstructured data.   Strategically   identify , quantify, and effectively communicate  emerging risk from aggregated data not   identified   by the enterprise in isolated processes to drive proactive risk mitigation.   Lead collaboration efforts  with risk managers, quantitative analysts, and business stakeholders to integrate AI solutions into strategic decision-making processes.   Lead all aspects of  risk and control analysis and validation in line with established standards, providing comprehensive risk mitigation recommendations and strategic guidance.   Drive and oversee  remediation efforts related to audit, compliance, and regulatory findings,  establish  the quarterly audit process, and  manage  procedural implementation and change management to ensure sound governance and controls.   Initiate and lead efforts to enhance and automate  control   processes, and   oversee the monitoring of control exceptions and breaches.   Establish and actively promote  strong governance, controls, and a culture of responsible finance,  leading   the implementation and oversight  of the Control Framework.   Recommended Qualifications   Core AI Concepts:   Generative AI (GenAI):  Deep understanding and practical application of generative models.   Agentic AI:  Experience in building and deploying autonomous AI agents.   Retrieval-Augmented Generation (RAG):   Expertise   in   leveraging   RAG for enhanced information synthesis.   Knowledge Graphs:  Proven ability to construct and   utilize   knowledge graphs for complex data representation.   Technical Skills and Qualifications:   Programming & Frameworks:   Proficiency   in:  Python   Good to Have Libraries:   LangChain ,   LangSmith ,   LangGraph ,   Streamlit ,   PyTorch ,   FastAPI .   Database Technologies:   Good to Have:  Graph Databases (Neo4j), Vector Databases ( PGVector , Milvus, Pinecone)   Relational Databases:  PostgreSQL, SQL   Unstructured Data Expertise:  Ability to extract, clean, transform, and analyze unstructured data from diverse sources such as customer complaints, issues, etc.   Natural Language Processing & Machine Learning Skills:   Expertise   in text preprocessing (tokenization, stemming, lemmatization), named entity recognition, sentiment