Head of Data & AI Section

1.Job Purpose To lead and develop the organization's Data and Artificial Intelligence strategy, oversee the establishment and management of enterprise data infrastructure, and drive data governance, data quality, integration, advanced analytics, and AI capabilities. The role is responsible for leveraging data and artificial intelligence technologies to enable data-driven decision-making, improve operational efficiency, enhance customer experience, and create business value through innovation and digital transformation. The position also leads data and AI initiatives and projects, ensuring alignment with corporate strategy, regulatory requirements, and organizational objectives while maximizing the return on investment from data and AI capabilities. 2. Key Responsibilities and Duties A. Strategy and Leadership Develop and implement the organization's Data and Artificial Intelligence strategy in alignment with corporate objectives. Establish a strategic roadmap for data management, analytics, and AI initiatives. Lead the organization's transformation toward a data-driven culture. Identify strategic priorities, investment opportunities, and high-value initiatives. Develop operational plans, budgets, and performance targets. Monitor strategic execution and report progress to senior management. B. Data Management and Governance Develop and implement an enterprise-wide Data Governance Framework. Oversee data quality, accuracy, completeness, consistency, and integrity. Establish policies, standards, and procedures for data management, classification, access, and sharing. Supervise Master Data Management (MDM) and Reference Data Management initiatives. Ensure compliance with applicable data protection, privacy, and regulatory requirements. Define data ownership, stewardship, and accountability roles. Oversee metadata management, business glossaries, and data dictionaries. C. Data Engineering and Infrastructure Oversee the design and development of enterprise data architecture. Lead the implementation and management of Data Warehouses, Data Lakes, and modern Lakehouse platforms. Supervise ETL/ELT processes and data integration activities. Ensure seamless data integration across enterprise systems and platforms. Oversee the development and maintenance of reliable data pipelines. Ensure data platform scalability, availability, performance, and reliability. Evaluate and recommend appropriate data technologies, tools, and platforms. D. Artificial Intelligence and Machine Learning Define and implement the organization's Artificial Intelligence adoption strategy. Identify business and operational opportunities for AI implementation. Lead the development and deployment of Machine Learning models. Oversee Generative AI applications and Large Language Models (LLMs). Drive intelligent automation, predictive analytics, and advanced AI solutions. Supervise the AI model lifecycle, including development, testing, deployment, and monitoring. Ensure responsible, ethical, secure, and compliant AI adoption. Monitor model performance, accuracy, reliability, and potential biases. E. Business Intelligence and Analytics Lead the development and implementation of Business Intelligence capabilities. Oversee the design and delivery of executive dashboards and management reports. Establish Key Performance Indicators (KPIs) and enterprise performance monitoring dashboards. Enable business departments to access, analyze, and utilize data effectively. Deliver predictive and prescriptive analytics to support strategic decision-making. Translate complex data into actionable business insights and recommendations. F. Project Management and Innovation Manage the portfolio of Data and AI projects and initiatives. Ensure projects are delivered within approved timelines, budgets, and quality standards. Conduct technical and financial feasibility assessments for proposed initiatives. Monitor and evaluate the Return on Investment (ROI) of data and AI projects. Explore emerging technologies and assess their business value. Build strategic partnerships with technology vendors, consultants, and specialized service providers. G. Team Management and Organizational Development Build, lead, and manage a high-performing Data and AI team. Identify workforce requirements and technical competency needs. Allocate responsibilities, monitor performance, and ensure effective team delivery. Develop employees through training, professional development, and knowledge transfer. Promote innovation, collaboration, and continuous learning. Establish succession planning and technical leadership development programs. H. Data Security, Compliance, and Risk Management Coordinate with the Cybersecurity Department to ensure data protection. Ensure appropriate access controls, permissions, and sensitive data management practices. Assess data and AI-related risks and implement mitigation plans. Ensure compliance with applicable laws, regulations, and organizational standards. Monitor privacy and personal data protection requirements. 3. Authorities and Management Responsibilities Propose and manage departmental operational plans within approved authorities. Recommend the selection and adoption of data and AI technologies and platforms. Manage departmental human, technical, and financial resources. Supervise technology vendors, contracts, and service providers within delegated authority. Review and approve technical project deliverables before submission for final approval. Prepare and submit periodic performance reports and strategic recommendations to senior management. Represent the department in relevant strategic committees and organizational initiatives. 4. Educational Qualifications Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Science, Artificial Intelligence, Information Systems, or a related discipline. Master's degree in Data Science, Artificial Intelligence, Information Technology Management, or a related field is preferred. Relevant professional certifications in Data Governance, Data Engineering, Artificial Intelligence, or Project Management are preferred. 5. Professional Experience Minimum of 8 years of professional experience in data, IT, advanced analytics, or related fields. Minimum of 3 years in managerial or leadership positions. Proven experience in developing and implementing Data and AI strategies. Experience managing enterprise data platforms and digital transformation projects. Practical experience in AI applications, Machine Learning, and advanced analytics. Demonstrated experience in managing technical teams, budgets, and external vendors. 6. Required Skills and Competencies Leadership and Management Competencies Strategic leadership and forward thinking. Business planning and prioritization. Change management and organizational transformation. Project and budget management. Decision-making and problem-solving. Effective communication, negotiation, and stakeholder management. Technical and Functional Competencies Data Governance & Data Management. Data Architecture & Data Engineering. Data Warehousing & Data Lakes. SQL & Python. Machine Learning & Deep Learning. Generative AI & Large Language Models. Business Intelligence & Data Analytics. MLOps & DataOps. Cloud Data Platforms. Data Security & Privacy. 7. Key Performance Indicators (KPIs) KPISuggested Target Data and AI Strategy Implementation 90% or above Data Quality Score 95% or above Compliance with Data Governance Policies 95% or above Data and AI Project Delivery Rate 90% or above AI Model Success Rate Based on individual model targets BI Dashboard and Analytics Adoption Annual growth target Reduction in Reporting Preparation Time 30% or above Return on Investment (ROI) of AI Projects Based on approved business cases Internal Stakeholder Satisfaction 90% or above Data Security and Privacy Compliance 100% Show more Show less