Head of Data and Analytics Job

Principle Accountabilities

Formulation of a bank-wide data strategy that supports business growth, risk management, compliance, and customer experience. This includes:

• Defining Data Objectives: Align data initiatives with business goals (e.g., customer analytics, risk modeling, fraud detection).
• Data Monetization Strategy: Identifying ways to use data for competitive advantage (e.g., personalized banking products, credit scoring).
• Collaboration with CIO: Working closely with the Chief Information Officer (CIO) and to ensure technological and operational feasibility.
• Custody & Governance of Data : As the custodian of the data strategy, the division ensures that data is secure, high-quality, and regulatory-compliant by:
• Establishing Data Governance Policies: Ensuring data accuracy, integrity, and security.
• Regulatory Compliance: Overseeing compliance with data-related regulations (e.g., GDPR, CCPA, Basel III, local banking laws).
• Data Ethics & Customer Trust: Setting guidelines for ethical data usage, transparency, and customer privacy.
• Implementation of Data Strategy : drives the execution of data-driven transformation across the bank’s retail and commercial divisions by:
• Enhancing Data Infrastructure: Supporting cloud migration, data lakes, and AI-driven analytics.
• Embedding Data in Decision-Making: Ensuring that all departments use data insights for lending, risk assessment, marketing, and operations.
• Customer & Market Insights: Leveraging data for customer segmentation, hyper-personalization, and predictive banking.
• Risk & Fraud Management: Implementing AI/ML models for credit scoring, anti-money laundering (AML), and fraud detection.
• Performance Monitoring & Adaptation.
• Tracking Data-Driven KPIs: Measuring the impact of data initiatives on revenue, cost reduction, and customer engagement.
• Continuous Optimization: Adapting the data strategy to emerging trends like open banking, real-time payments, and AI-powered risk modeling.
• Cross-Functional Leadership: Aligning departments (IT, finance, risk, operations) to ensure seamless data utilization.
• Business Performance Monitoring & Reporting
• Tracks key performance indicators (KPIs) such as revenue growth, cost-to-income ratio (CIR), net interest margin (NIM), customer retention, and digital adoption.
• Development & automation of balanced scorecards and dashboards to track performance at all levels of the company.
• Develops dashboards and real-time reporting tools to give executives visibility into business performance.
• Provides insights into branch performance, digital channel efficiency, and product profitability.
• Measuring Performance in Customer-Facing Roles including;
• Sales & Revenue Performance.
• Customer Experience & Service Quality.
• Operational Efficiency in Retail & Business Banking.
• Measuring Performance in Back-Office Roles.
• Advanced Data Analytics & Predictive Modelling.
• Uses AI and machine learning to forecast customer behavior, credit risk, and product demand.
• Conducts profitability analysis to identify high-margin products and services.
• Implements predictive analytics to improve loan underwriting, fraud detection, and churn prediction.
• Customer Insights & Personalization
• Analyzes customer spending, transaction patterns, and lifestyle preferences to drive personalized banking experiences.
• Supports targeted marketing campaigns by identifying high-value customer segments.
• Improves cross-selling and upselling strategies to increase product penetration.
• Astute people leadership
• Hire, lead, and develop a high-performing team of data scientists, engineers, and analysts.
• Collaborate with business units (e.g., Risk, Marketing, Finance) to translate data insights into actionable strategies.
• Foster a data-driven culture throughout the bank, encouraging data literacy and evidence-based decision-making.

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Key Competencies and Skills

General, Technical & Leadership Competencies

• Proficiency in database management (SQL, NoSQL), data warehousing, and analytics tools (e.g., Power BI, Tableau).
• Familiarity with cloud-based data platforms (AWS, Azure, Google Cloud).
• Hands-on experience with machine learning models, predictive analytics, and statistical techniques.
• Proficiency in data science programming languages (Python, R, SAS).
• Knowledge of big data ecosystems, including Apache Kafka, Apache Spark, and Hadoop.
• Ability to design interactive dashboards and self-service analytics solutions.
• Experience in KPI tracking, reporting automation, and visualization best practices.
• Ability to implement scalable data solutions for high-volume transactions.
• Data Governance and Compliance.
• Strong business acumen and strategic thinking.
• Ability to adapt to changing technologies and industry trends.
• Lead, mentor, and develop a team of data analysts and data scientists to achieve departmental goals and foster a culture of learning and growth.

Minimum Qualifications, Knowledge and Experience

Academic and Professional Qualifications

• Bachelor’s degree in Computer Science, Data Science, Mathematics, Business Analytics, or a related field.
• Master’s degree or MBA is preferred.

Experience

• 8-12 years of progressive experience in data analytics, data management, or business intelligence.
• At least 3–5 years in a leadership role, preferably in banking or financial services / Proven leadership experience in cross-functional or enterprise-level data initiatives.
• Strong knowledge of data governance, data warehousing, and regulatory compliance in the banking sector.
• Expertise in BI tools (Tableau, Power BI, etc.), SQL, Python/R, and cloud-based analytics platforms.
• Experience with AI, machine learning, and big data frameworks is a plus.
• Strong understanding of data governance, data quality, and regulatory requirements.

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How to Apply

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