HFC Limited, the banking and property finance subsidiary of HF Group, has an exciting opportunity in our Strategy and Business Performance Department. We are seeking a talented, dynamic, self-driven, and results-oriented individual who is committed to performance, excellence, and participating in our growth strategy.
The Head of Data and Analytics is a strategic leadership role responsible for leading the development, execution, and governance of the organization’s data and analytics strategy, ensuring that data becomes a strategic asset that informs decision-making, drives business performance, and supports digital transformation. The role will lead enterprise-wide data initiatives, oversee data governance, and enable advanced analytics capabilities and commercialization to unlock value across the Group.
Deadline: 2025-06-08
Category: Strategy & Business Performance
Subsidiary: HFC
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.
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.
