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.
Job Description
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.
Responsibilities
- Lead the design, development, and strategic deploymentof advanced AI and machine learning models to identify, analyze, and monitor emerging thematic risks across global markets.
- Drive the conception and implementationof sophisticated Agentic AI systems for autonomous and proactive risk detection, analysis, and alerting.
- Architect and oversee the managementof 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 developmentof 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 communicateemerging risk from aggregated data not identified by the enterprise in isolated processes to drive proactive risk mitigation.
- Lead collaboration effortswith risk managers, quantitative analysts, and business stakeholders to integrate AI solutions into strategic decision-making processes.
- Lead all aspects ofrisk and control analysis and validation in line with established standards, providing comprehensive risk mitigation recommendations and strategic guidance.
- Drive and overseeremediation efforts related to audit, compliance, and regulatory findings,establishthe quarterly audit process, andmanageprocedural implementation and change management to ensure sound governance and controls.
- Initiate and lead efforts to enhance and automatecontrol processes, and oversee the monitoring of control exceptions and breaches.
- Establish and actively promotestrong governance, controls, and a culture of responsible finance,leading the implementation and oversightof 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 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 toGoogle Cloud Platform (GCP) or Amazon Web Services (AWS) is required.
Experience and Competencies
- 10+ years of experiencein Data Science, with banking and finance experience preferred but not mandatory.
- Demonstrated leadershipin establishing strong governance and controls, and fostering a culture of responsible finance, good governance, and ethics.
- Proven track recordof designing and leading complex projects that significantly enhance processes, showcasing exceptional creativity in problem-solving.
- Maintains expert knowledge of evolving requirements and their impacts, responsible for significant business results and technical strategy.
- Strong leadership skills to manage governance and foster a culture of responsible finance and ethics.
- Exceptional communication and stakeholder management skills to effectively liaise with various stakeholders across the business.
Education
- Bachelor's/University degree, Master's degree preferred.
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Job Family Group
Risk Management
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Job Family
Regulatory Risk
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Time Type
Full time
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Most Relevant Skills
Analytical Thinking, Credible Challenge, Governance, Policy, Procedure, and Regulation, Risk Management Lifecycle, Stakeholder Management.
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Other Relevant Skills
Constructive Debate, Escalation Management, Financial Analysis, Issue Management, Management Reporting, Policy and Procedure, Policy and Regulation, Risk Controls and Monitors, Risk Identification and Assessment.
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