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Urgent! Head of Data and Analytics at HF Group Job Opening In Kenya, Kenya – Now Hiring Confidential

Head of Data and Analytics at HF Group



Job description

Job Description

Housing Finance Company of Kenya was incorporated as the premier mortgage Finance Institution in Kenya licensed under the Banking Act with the CDC and the GoK owning 60% and 40% respectively.


Housing Finance started operations with the main objective of implementing the government’s policy of promoting thrift and home ownership by lending …


Head of Data and Analytics

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.

  • Required Skill Profession

    Other General



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