Thematic Risk Analytics Sr Analyst - Assistant Vice President

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 overallobjectivesof the bank.

This role is a unique and exciting opportunity to contribute to building the future of thematic risk usingcutting-edgedata science and AI.

Responsibilities

• Develop and deploy advanced AI and machine learning models toidentify, analyze, andmonitoremerging thematic risks across global markets, contributing to their design.

• Implement and contribute to the building of sophisticated Agentic AI systems for autonomous and proactive risk detection, analysis, and alerting.

• Contribute to the construction and 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.

• Contribute to the development of proof-of-concepts and rapidly prototype new AI-driven risk management tools and platforms.

• Execute analysis of large-scale data populations aggregated from target platforms, processes, and product lines, consisting of structured and unstructured data, with guidance on design as needed.

• Effectivelyidentify, quantify, and communicate emergingriskfrom aggregated data notidentifiedby the enterprise in isolated processes to support proactive risk mitigation.

• Collaborate with risk managers, quantitative analysts, and business stakeholders to integrate AI solutions into strategic decision-making processes.

Recommended Qualifications

Core AI Concepts:

• Generative AI (GenAI): Understanding and practical application of generative models.

• Agentic AI: Experience in building and deploying autonomous AI agents.

• Retrieval-Augmented Generation (RAG): ExpertiseinleveragingRAG for enhanced information synthesis.

• Knowledge Graphs: Proven ability to construct andutilizeknowledge graphs for complex data representation.

Technical Skills and Qualifications:

• Programming & Frameworks:

• Proficiencyin: 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: Expertisein text preprocessing (tokenization, stemming, lemmatization), named entity recognition, sentiment analysis, and applying Machine Learning algorithms like classification, clustering, and topic modeling.

• Insights & Reporting: Experience converting processed unstructured data into actionable insights using visualizations, dashboards, and automated reporting tools.

• Exposure to Google Cloud Platform (GCP) or Amazon Web Services (AWS) isrequired.

Experience and Competencies:

• 8+ years of experience in Data Science, with banking and finance experience preferred but not mandatory.

• Experience in promoting strong governance and controls, and contributing to a culture of responsible finance, good governance, and ethics.

• Proven ability to contribute to and execute projects that enhance processes,demonstratingproblem-solving in complex situations.

• Maintains knowledge of evolving requirements and their impacts, contributing to business results and technical strategy.

• Strong communicationskills to liaise with various stakeholders across the business.

Education

• Bachelor's/University degree,Master'sdegree preferred.