Job Summary
The Director, Advanced Analytics & AI is a techno-functional leader responsible for designing, building, and industrialising advanced analytics and machine learning solutions that enhance the bank’s financial risk management, regulatory compliance, and decision-making capabilities.
This role sits at the intersection of Business (Risk / Compliance), CDO (data products), and Technology (engineering & product industrialisation), ensuring an end-to-end lifecycle from use case discovery to production-grade deployment, aligned to regulatory and model governance expectations.
Key Responsibilities
Strategy
- Lead identification, prioritisation, and shaping of high-impact analytics & ML use cases across Financial Risk and Compliance domains.
- Translate regulatory and business requirements into analytical problem statements and solution blueprints.
- Own business value realisation (efficiency, risk reduction, control effectiveness, insights).
- Aligns with CoE mandate to drive value-led, outcome-focused AI delivery.
Business
- Define end-to-end solution architecture for analytics and ML use cases: Feature engineering, model selection, evaluation strategy, data sourcing, and transformation requirements.
- Establish and enforce design patterns, reusable components, and modelling standards.
- Reflects role of lead architect + capability owner in CoE model.
Processes
- Personally lead or closely supervise the development of POCs and prototypes for new analytical patterns and complex or regulatory-sensitive use cases.
- Validate feasibility, performance, and explainability, with a core expectation of prototype → validate → scale recommendation.
- Establish reusable feature engineering pipelines, model templates, and evaluation frameworks. Drive scaling from POCs to enterprise-grade solutions, critical to avoid “one-off analytics” and move to repeatable AI products.
- Define requirements for AI-ready data products with CDO teams: curated datasets, feature stores, data quality & lineage.
- Ensure alignment between data supply (CDO) and analytics consumption (AI CoE), aligning with CoE positioning as bridge between data and intelligence.
People & Talent
- Lead through example and demonstrate the bank’s culture and values.
Risk Management
- Work with Technology and Data Engineering teams to industrialise solutions into production.
- Provide oversight for model integration, pipelines, APIs, and deployment frameworks.
- Ensure functional correctness and alignment to business intent, consistent with model Risk/AI CoE owning logic and validation, and Technology owning runtime & engineering.
- Embed analytics into credit risk models, stress testing & forecasting, financial crime detection, and regulatory reporting analytics.
- Ensure outputs are explainable, auditable, and regulator-ready.
Governance
- Define and enforce end-to-end model lifecycle controls: model documentation and explainability, validation frameworks, monitoring (drift, bias, performance).
- Ensure compliance with Model Risk Management, AI governance, fairness, explainability, and regulatory expectations on AI usage, with a strong emphasis on governed lifecycle and audit-readiness.
Reporting & Stakeholder Communication
- Act as primary interface between business stakeholders, CDO, and Technology.
- Engage senior stakeholders to align priorities, drive adoption, and manage regulatory expectations. This role explicitly requires strong business-tech bridging capability.
Team Leadership & Capability Building
- Lead multidisciplinary teams of data scientists, ML engineers, and analytics specialists.
- Coach teams on model development best practices, regulatory constraints, and production readiness.
- Build reusable frameworks, accelerators, and experimentation standards.
Skills and Experience
Technical and Operational Skills
- Strong hands-on experience in Machine Learning (Classification, Regression, Clustering, Anomaly Detection), Time Series Modelling and Forecasting, and NLP / Text Analytics (for compliance and surveillance use cases).
- Proficiency in Python / R / SQL and Big Data Frameworks (e.g., Spark).
- Deep understanding of Model Lifecycle (Development → Validation → Deployment → Monitoring) and MLOps (CI/CD, Versioning, Monitoring), and Data Management (Quality, Lineage, Governance).
- Strong experience in Financial Risk (credit risk, stress testing, exposure analytics) and Regulatory compliance / financial crime analytics.
- Solid understanding of Model Risk Management frameworks and regulatory expectations on AI, models, and data.
Role Specific Technical Competencies
- GenAI/agentic concepts.
- Product & portfolio management (intake, prioritisation, lifecycle, adoption).
- AI risk management literacy (validation, drift/monitoring concepts).
- Stakeholder management and operating model design.
Leadership & Delivery Experience
- Proven track record of delivering analytics solutions from POC to production and leading cross-functional teams across business, data, and technology.
- Experience operating in matrixed environments (business + CDO + tech).
AI Governance & Responsible AI
- Strong understanding of explainability, bias/fairness, ethical AI, and audit and control requirements.
Problem Solving & Strategic Thinking
- Ability to break down complex business problems into analytical solutions and balance technical sophistication with regulatory and operational constraints.
Qualifications
- Degree in Data Science, Statistics, Mathematics, Engineering, Computer Science or related field.
- Postgraduate (Masters/PhD) in quantitative discipline preferred.
- Extensive programming experience using SQL, Python, SAS, Excel Automation.
- Strong analytical mindset with excellent analytical, logical, reasoning and problem-solving skills.
- Excellent written and oral communication skills at all levels and situations.
- Exposure to advanced machine learning methodologies is a plus.