Overview

The AVP — Data Analytics & AI is a hands-on technical leadership role within the Risk & Compliance division, responsible for designing, building, and deploying advanced analytics and AI solutions across risk domains.

Responsibilities

• Design, develop, and deploy machine learning models and data analytics solutions for credit risk, financial crime, operational risk, and compliance monitoring use cases.
• Build and maintain end-to-end ML pipelines — from data ingestion and feature engineering through model training, validation, and deployment.
• Develop NLP and generative AI applications including RAG-based document retrieval, automated regulatory analysis, and compliance report generation.
• Deliver predictive analytics capabilities such as early warning models, anomaly detection, and risk scoring enhancements.
• Create and maintain BI dashboards and analytical reports using Power BI, Tableau, or equivalent tools.
• Develop well-documented, reproducible models that meet internal Model Risk Management (MRM) validation standards.
• Prepare model documentation packages including methodology papers, validation reports, and ongoing monitoring plans.
• Support the VP in regulatory exam preparedness and AI governance activities, including bias testing and explainability reporting.
• Contribute to the maintenance of model inventories and performance monitoring frameworks.
• Collaborate with Data Engineering and Cloud Platform teams to build scalable data pipelines and ensure data quality for analytics consumption.
• Work with cloud-native platforms (AWS/Azure) and big data technologies to process and transform large-scale risk datasets.
• Contribute to feature store development and data quality monitoring aligned with BCBS 239 principles.
• Partner with risk officers, compliance analysts, and business SMEs to understand requirements and translate them into analytical solutions.
• Present model outputs, analytical findings, and technical recommendations to senior stakeholders in clear, non-technical language.
• Collaborate with cross-functional teams including Technology, Chief Data Office, and Front Office.
• Mentor and guide junior data scientists and analytics engineers, conducting code reviews and knowledge-sharing sessions.
• Contribute to hiring, onboarding, and technical competency development within the analytics team.
• Stay current with emerging AI/ML research, tools, and techniques — bringing best practices into the team.
• Identification, collection, extraction of data from various sources, including internal and external sources.
• Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis.
• Development and maintenance of efficient data pipelines for automated data acquisition and processing.
• Design and conduct of statistical and machine learning models to analyse patterns, trends, and relationships in the data.
• Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities.
• Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science.
• To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
• Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.
• OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes.
• Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
• Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.

Qualifications

• Proven track record of delivering production-grade ML models that have driven measurable business impact.
• Solid understanding of banking risk concepts including credit risk (PD/LGD, IFRS 9), market risk, operational risk, or financial crime (AML, fraud detection).
• Some exposure to regulatory frameworks such as BCBS 239, Basel III/IV, or PRA/FCA guidance on AI/ML.
• Strong proficiency in Python for ML development and data analysis.
• Solid SQL skills and experience with big data technologies (Spark, Databricks).
• Hands-on experience with ML frameworks — scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
• Practical experience with NLP and/or generative AI — LLMs, RAG, prompt engineering.
• Working knowledge of cloud platforms (AWS SageMaker, Azure ML) and MLOps tooling (MLflow, Airflow).
• Familiarity with version control (Git) and collaborative development practices.
• Knowledge of graph analytics or network analysis for financial crime detection.
• Exposure to real-time streaming technologies (Kafka, Flink).
• Prior involvement in model validation reviews or regulatory examinations.
• Familiarity with emerging AI regulations (EU AI Act, UK AI regulatory framework).
• Published research or open-source contributions in ML or data science.
• Strong problem-solving skills with the ability to break down complex business problems into analytical approaches.
• Intellectually curious with a passion for staying at the forefront of AI/ML research and tooling.
• Rigorous approach to code quality, reproducibility, and documentation.
• Ability to explain technical concepts and model outputs to non-technical stakeholders clearly and concisely.
• Strong team player who thrives in cross-functional environments spanning Risk, Compliance, and Technology.
• Effective written communication skills for model documentation and technical reports.
• Self-starter who can manage multiple workstreams and deliver under deadlines.
• Proactive in identifying risks, blockers, and opportunities for improvement.
• Comfortable balancing hands-on technical delivery with mentoring and coordination responsibilities.
• Appreciation for the regulatory environment in banking and the importance of model governance.
• Commitment to responsible use of AI and data, with attention to fairness, privacy, and ethical considerations.

Education

• Master's degree in a quantitative discipline — Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering, or a related field. PhD is a plus but not required.
• Relevant certifications are advantageous (e.g., AWS ML Specialty, Azure Data Scientist, FRM).