Lead Specialist Data & Artificial Intelligence Delivery Management

About Ma'aden and the Role

Ma'aden, established in 1997 as the Saudi Arabian Mining Company, is a multi-commodity mining and metals company in the Middle East. It contributes to the development of the mining industry as a pillar of Saudi Arabia’s economy by building an integrated mining value chain. This full-time role is for a Lead Specialist, Data & Artificial Intelligence Delivery Management, based in Riyadh, requiring 6-8 years of experience.

Role Purpose

The Lead Specialist, Data Science & Analytics, is responsible for driving the development, deployment, and adoption of advanced analytics, machine learning, and AI solutions. These solutions aim to improve operational performance, business decision-making, and enterprise productivity across Ma'aden. The role involves transforming complex business challenges into scalable analytical solutions that generate measurable value across various functions, including mining, processing, operations, and corporate. It bridges business priorities with technical execution, ensuring analytical products progress efficiently from concept through deployment and industrialization.

Key Responsibilities

• Lead the end-to-end development of machine learning, AI, and advanced analytics solutions addressing high-value business challenges.
• Deliver scalable, production-ready models that enhance operational efficiency, reliability, productivity, and decision-making.
• Strengthen analytical capabilities through robust experimentation, model optimization, and continuous performance improvement.
• Translate business priorities into actionable analytics opportunities, value hypotheses, and implementation roadmaps.
• Identify, prioritize, and develop AI and analytics use cases that deliver measurable operational and financial outcomes.
• Establish clear success measures, KPIs, and value realization frameworks for analytical initiatives.
• Enable seamless deployment of analytical models into enterprise systems, operational platforms, and digital environments.
• Strengthen model reliability and scalability through effective MLOps practices, lifecycle management, and monitoring standards.
• Improve adoption and sustainability of AI solutions by ensuring technical readiness and operational integration.
• Strengthen trust in analytics through reliable, governed, and high-quality data foundations.
• Partner with Data Engineering teams to improve analytical environments, data pipelines, and model readiness.
• Ensure data assets meet enterprise requirements for accuracy, integrity, security, and compliance.
• Embed responsible AI principles, governance controls, and quality standards across analytical solutions.
• Strengthen transparency, explainability, auditability, and lineage across data science assets and models.
• Ensure compliance with enterprise data governance, security, and regulatory requirements.
• Build strong partnerships with business, technology, and Data & AI stakeholders to accelerate solution adoption.
• Communicate analytical insights, risks, recommendations, and outcomes through compelling narratives and visualizations.
• Measure and improve business impact through continuous tracking of adoption, performance, and value realization.

Measures of Success

• Machine learning and AI solutions deliver measurable operational, financial, and productivity improvements.
• Analytical products are successfully adopted and sustained across targeted business functions.
• Models achieve required standards for performance, reliability, scalability, and business relevance.
• AI and analytics projects progress efficiently from concept to production deployment.
• Data science initiatives demonstrate compliance with governance, security, and Responsible AI requirements.
• Business stakeholders make faster and better decisions through trusted analytical insights and predictive capabilities.

Experience Requirements

Candidates for this role should possess 6-8 years of relevant professional experience in data and artificial intelligence delivery management.