Job Purpose

To design, develop, deploy and govern advanced analytics, machine learning and artificial intelligence solutions that support strategic decision-making, strengthen risk management, improve customer outcomes, optimize financial performance and enhance operational efficiency across the Bank. The role will leverage structured and unstructured data to build predictive and prescriptive models, translate complex analysis into actionable business recommendations, and embed scalable, responsible and measurable analytics solutions into business processes. The job holder will partner with Business, Finance, Risk, Compliance, Operations and Technology teams to strengthen the Bank’s advanced analytics capability and promote data-driven decision-making.

Key Responsibilities

• Design and develop predictive and prescriptive models for priority banking use cases, including customer behaviour, credit and portfolio risk, fraud and anomaly detection, customer churn, collections, deposit attrition, product propensity, profitability and branch or channel optimization.
• Perform data discovery, preparation, feature engineering, model training, validation, tuning and testing using structured and unstructured banking data.
• Apply appropriate statistical, machine learning, forecasting, optimization and experimentation techniques to uncover patterns and support evidence-based decisions.
• Translate complex analytical outputs into clear recommendations, decision rules, dashboards and interventions for business and control functions.
• Evaluate requests for new or modified analytical solutions to determine business value, feasibility, data availability, delivery effort, dependencies and compatibility with the Bank’s technology environment.
• Develop proof-of-concepts and pilots to assess emerging analytics and AI approaches before scaled implementation.
• Collaborate with Data Engineering and Technology teams to productionize models and analytical solutions using scalable and controlled deployment practices.
• Apply version control, reproducible development, testing, release management and model lifecycle practices appropriate to the Bank’s environment.
• Monitor model accuracy, stability, drift, bias, fairness and operational performance, and implement remediation, retraining or retirement actions when required.
• Maintain a complete inventory of analytical models, including ownership, purpose, status, dependencies, versions and review dates.
• Establish and maintain fit-for-purpose model documentation covering methodology, assumptions, limitations, data sources, validation results, performance thresholds and intended use.
• Support independent model review and validation by providing evidence, test results and traceable documentation.
• Ensure analytics and AI solutions comply with applicable laws and regulations, internal policies, information security requirements, data protection obligations, model risk controls and responsible AI principles.
• Assess and communicate model limitations, explainability, potential bias and risks to relevant stakeholders before implementation.
• Maintain audit-ready records and ensure material model changes are appropriately reviewed and approved.
• Partner with Business, Finance, Risk, Compliance, Operations and Technology stakeholders to define business problems, analytical hypotheses, success measures and implementation requirements.
• Quantify and track the benefits of analytics initiatives, including revenue uplift, cost reduction, risk mitigation, customer retention, productivity improvement and turnaround-time reduction.
• Prioritize analytical use cases using strategic alignment, expected value, feasibility, data readiness, risk and implementation effort.
• Act as a trusted analytical advisor by challenging assumptions and converting findings into practical actions and decisions.
• Work with business owners to formulate key business questions, define consistent metrics and build trusted datasets that answer those questions.
• Integrate predictive outputs into management reporting and performance monitoring where they improve forward-looking decision-making.
• Promote adoption of Business Intelligence tools, analytical frameworks and self-service analytics across the Bank.
• Deliver performance dashboards and analytical insights covering deposits, financing growth, revenue streams, profitability, portfolio quality, customer performance, products, branches and channels.
• Work with business owners to formulate key business questions, define consistent metrics and build trusted datasets that answer those questions.
• Integrate predictive outputs into management reporting and performance monitoring where they improve forward-looking decision-making.
• Work with Data Governance, Data Engineering and business data owners to improve the quality, lineage, accessibility, security and usability of data used for analytics.
• Define analytical data requirements, critical data elements, quality rules and remediation priorities for key data science use cases.
• Improve procedures, guidelines, standards and reusable practices for analytical data management, maintenance, reporting and security.
• Contribute to the enhancement of the Bank’s analytics architecture, tools, development environments and scalable data science platforms.
• Contribute to the Bank’s advanced analytics and AI roadmap and identify high-value opportunities for machine learning, natural language processing, generative AI and intelligent automation.
• Develop and maintain user guides, technical documentation, model cards, operating procedures and knowledge repositories for analytical solutions.
• Deliver user training and knowledge-sharing sessions to enable stakeholders to understand, interpret and responsibly use analytical outputs.
• Mentor junior analysts and contribute to professional standards, peer review and continuous capability development within the Data & Analytics function.
• Represent Data & Analytics in relevant project teams, governance forums and cross-functional working groups.
• Ensure compliance with all applicable laws and regulations, as well as with the Bank’s internal policies and procedures while executing assigned duties and responsibilities.
• Perform any other duties that may be assigned by management, consistent with the purpose and level of the role.

Qualifications and Experience

• Bachelor’s degree in Statistics, Applied Mathematics, Data Science, Computer Science, Actuarial Science, Economics, Financial Engineering, Operations Research or another relevant quantitative discipline.
• Master’s degree in Data Science, Statistics, Artificial Intelligence, Business Analytics, Financial Engineering or a related field will be an added advantage.
• Relevant professional certifications in data science, cloud analytics, machine learning, business intelligence, data management, risk or banking will be an added advantage.
• Minimum of 3 to 5 years’ relevant experience in banking analytics, data science, risk analytics, customer analytics or a closely related quantitative role.
• Demonstrable experience delivering analytical solutions from business problem definition through model development, deployment, adoption and benefits tracking.

Key Behavioral Competencies

• Analytical thinking, intellectual curiosity and sound professional judgement.
• Ability to communicate complex analysis clearly to technical, business and executive audiences.
• Collaboration, stakeholder influence and constructive challenge.
• Attention to detail, documentation discipline and a strong control mindset.
• Commercial awareness, delivery focus and accountability for measurable outcomes.
• Commitment to ethical, fair and responsible use of data and AI.

• Design and develop predictive and prescriptive models for priority banking use cases, including customer behaviour, credit and portfolio risk, fraud and anomaly detection, customer churn, collections, deposit attrition, product propensity, profitability and branch or channel optimization.
• Perform data discovery, preparation, feature engineering, model training, validation, tuning and testing using structured and unstructured banking data.
• Apply appropriate statistical, machine learning, forecasting, optimization and experimentation techniques to uncover patterns and support evidence-based decisions.
• Translate complex analytical outputs into clear recommendations, decision rules, dashboards and interventions for business and control functions.
• Evaluate requests for new or modified analytical solutions to determine business value, feasibility, data availability, delivery effort, dependencies and compatibility with the Bank’s technology environment.
• Develop proof-of-concepts and pilots to assess emerging analytics and AI approaches before scaled implementation.
• Collaborate with Data Engineering and Technology teams to productionize models and analytical solutions using scalable and controlled deployment practices.
• Apply version control, reproducible development, testing, release management and model lifecycle practices appropriate to the Bank’s environment.
• Monitor model accuracy, stability, drift, bias, fairness and operational performance, and implement remediation, retraining or retirement actions when required.
• Maintain a complete inventory of analytical models, including ownership, purpose, status, dependencies, versions and review dates.
• Establish and maintain fit-for-purpose model documentation covering methodology, assumptions, limitations, data sources, validation results, performance thresholds and intended use.
• Support independent model review and validation by providing evidence, test results and traceable documentation.
• Ensure analytics and AI solutions comply with applicable laws and regulations, internal policies, information security requirements, data protection obligations, model risk controls and responsible AI principles.
• Assess and communicate model limitations, explainability, potential bias and risks to relevant stakeholders before implementation.
• Maintain audit-ready records and ensure material model changes are appropriately reviewed and approved.
• Partner with Business, Finance, Risk, Compliance, Operations and Technology stakeholders to define business problems, analytical hypotheses, success measures and implementation requirements.
• Quantify and track the benefits of analytics initiatives, including revenue uplift, cost reduction, risk mitigation, customer retention, productivity improvement and turnaround-time reduction.
• Prioritize analytical use cases using strategic alignment, expected value, feasibility, data readiness, risk and implementation effort.
• Act as a trusted analytical advisor by challenging assumptions and converting findings into practical actions and decisions.
• Work with business owners to formulate key business questions, define consistent metrics and build trusted datasets that answer those questions.
• Integrate predictive outputs into management reporting and performance monitoring where they improve forward-looking decision-making.
• Promote adoption of Business Intelligence tools, analytical frameworks and self-service analytics across the Bank.
• Deliver performance dashboards and analytical insights covering deposits, financing growth, revenue streams, profitability, portfolio quality, customer performance, products, branches and channels.
• Work with business owners to formulate key business questions, define consistent metrics and build trusted datasets that answer those questions.
• Integrate predictive outputs into management reporting and performance monitoring where they improve forward-looking decision-making.
• Work with Data Governance, Data Engineering and business data owners to improve the quality, lineage, accessibility, security and usability of data used for analytics.
• Define analytical data requirements, critical data elements, quality rules and remediation priorities for key data science use cases.
• Improve procedures, guidelines, standards and reusable practices for analytical data management, maintenance, reporting and security.
• Contribute to the enhancement of the Bank’s analytics architecture, tools, development environments and scalable data science platforms.
• Contribute to the Bank’s advanced analytics and AI roadmap and identify high-value opportunities for machine learning, natural language processing, generative AI and intelligent automation.
• Develop and maintain user guides, technical documentation, model cards, operating procedures and knowledge repositories for analytical solutions.
• Deliver user training and knowledge-sharing sessions to enable stakeholders to understand, interpret and responsibly use analytical outputs.
• Mentor junior analysts and contribute to professional standards, peer review and continuous capability development within the Data & Analytics function.
• Represent Data & Analytics in relevant project teams, governance forums and cross-functional working groups.
• Ensure compliance with all applicable laws and regulations, as well as with the Bank’s internal policies and procedures while executing assigned duties and responsibilities.
• Perform any other duties that may be assigned by management, consistent with the purpose and level of the role.

• Python or R
• SQL
• Power BI
• Microsoft Excel
• Statistical modelling
• Supervised and unsupervised learning
• Forecasting
• Anomaly detection
• Version control
• Cloud-based analytics platforms

• Bachelor’s degree in Statistics, Applied Mathematics, Data Science, Computer Science, Actuarial Science, Economics, Financial Engineering, Operations Research or another relevant quantitative discipline.
• Master’s degree in Data Science, Statistics, Artificial Intelligence, Business Analytics, Financial Engineering or a related field will be an added advantage.
• Relevant professional certifications in data science, cloud analytics, machine learning, business intelligence, data management, risk or banking will be an added advantage.