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.