Job description

Communication
Internal Communication:
Internal Business Units
All Internal Department
Senior Leadership Team
Purpose:
-     Innovation in AI Products and Services 
-     Customer Experience Enhancement:
-     Data Integration and Democratization
-     Predictive and Prescriptive Analytics
-     Change Management and Adoption of AI
-     Cloud and Scalable AI Infrastructure
-     Data Governance and Ethics

External Communication:
External IT Vendors 
Software Integrators

Occupational Health & Safety and Environment
Accountability:
Are accountable for their acts and omissions.
Responsibility:
To follow agreed safe systems of work; to follow training and instructions; and to report accidents, incidents and near misses.
Authority:
To stop work if they think the work is unsafe.
 

Strategic Leadership & Vision
•    Define and execute the organization’s Data Science & AI strategy in alignment with business goals.
•    Identify and prioritize opportunities where AI and data driven solutions enhance efficiency, customer experience, product offerings, and revenue generation.
•    Lead the design of a unified data and AI architecture, including the development of a scalable Data Lakehouse, semantic layers, feature stores, and streaming/batch data integration.
•    Drive the Generative AI roadmap—selecting LLM providers, designing RAG architectures, and enabling enterprise wide AI capabilities.
•    Oversee FinOps practices to optimize cloud and compute costs associated with AI, GPU workloads, and large scale data storage.
AI & Data Engineering Execution
•    Oversee the end to end development, validation, deployment, and lifecycle management of machine learning and AI models.
•    Implement CI/CD/CT pipelines to ensure automated, continuous, and reliable model updates.
•    Build and maintain vector database infrastructure to support semantic search, contextual AI, and long term knowledge retrieval.
•    Ensure data pipelines, platforms, and architectures are optimized for performance, scalability, and resilience.
Governance, Risk, Compliance & Ethics
•    Implement data governance, security, and privacy standards, including RBAC/ABAC and protection of PII and sensitive datasets.
•    Establish and monitor automated controls for model drift, fairness, explainability, and ethical use of AI.
•    Ensure compliance with relevant regulations (e.g., GDPR, HIPAA) and internal policies.
•    Maintain best practices for data quality, versioning, reproducibility, and auditability.
Innovation & Continuous Improvement
•    Lead experimentation with new algorithms, methods, and technologies to maintain a cutting edge AI ecosystem.
•    Promote a culture of continuous improvement, refining processes, tools, and models to enhance performance and business impact.
Team Leadership & Capability Building
•    Mentor and develop a high performing team of data scientists, AI engineers, and data professionals.
•    Oversee training and upskilling initiatives to ensure the team remains current with emerging technologies and methodologies.
Stakeholder Management & Communication
•    Serve as a strategic advisor between business and technical teams—translating business challenges into AI solutions and articulating AI outcomes clearly to non technical stakeholders.
•    Provide regular updates to leadership on progress, performance metrics, model outcomes, and business results.
•    Drive cross department collaboration to ensure successful adoption and integration of AI solutions.
Technology, Tools & Vendor Management
•    Lead the selection, integration, and management of AI platforms, data tools, and vendor partnerships.
•    Manage budgets for data science and AI initiatives, ensuring optimal allocation of resources.
•    Make decisions related to hiring, vendor selection, and solutions procurement that support strategic AI objectives.
 

Education & Professional Qualification:
Bachelor’s Degree in Computer Science, Data Science
Artificial Intelligence, Statistics, Mathematics, Engineering, Information Technology 
Master’s Degree (preferred):
Data Science,  Artificial Intelligence or Machine Learning, Computer Science,  Business Analytics, Statistics or Applied Mathematics,   Operations Research 
Professional Experience:
•     8 years of hands-on experience in data science, machine learning, or AI roles, including developing and deploying AI models and algorithms.
•    Experience with machine learning techniques, such as:
-    Supervised/Unsupervised Learning
-    Deep Learning (e.g., Neural Networks, CNNs, RNNs)
-    Natural Language Processing (NLP)
-    Computer Vision
Geographic Experience:
A plus
Computer Skills:
data science, machine learning,  AI, including developing and deploying AI models and algorithms
Language Skills:
Business fluent English
Arabic Language is an advantage.