Lead Specialist, Data & Artificial Intelligence Delivery Management

Maaden, established in 1997, is one of the fastest-growing mining companies in the world and the largest multi-commodity mining and metals company in the Middle East. We are leading the development of the mining industry to become the third pillar of Saudi Arabia’s economy by building a world-class, unique, and fully integrated mining value chain. We are pleased to share an exciting opportunity for the below mentioned position. This role offers a chance to contribute to our ambitious growth and play a key part in shaping the future of mining in the Kingdom. 1. Why This Role Matters The Lead Specialist, Data Science & Analytics drives the development, deployment, and adoption of advanced analytics, machine learning, and AI solutions that improve operational performance, business decision-making, and enterprise productivity across Maaden. The role transforms complex business challenges into scalable analytical solutions that generate measurable value across mining, processing, operations, and corporate functions. The role bridges business priorities with technical execution, ensuring that analytical products progress efficiently from concept through deployment and industrialization. By embedding AI and data science capabilities into operational processes, the role accelerates digital transformation and strengthens Maaden’s ability to leverage data as a strategic asset. Through technical leadership, governance oversight, and stakeholder engagement, the role ensures analytical solutions are reliable, scalable, compliant, and aligned with enterprise standards, enabling sustainable adoption and value realization across the organization. 2. What You Will Deliver AI & Data Science Solution Delivery - Lead the end-to-end development of machine learning, AI, and advanced analytics solutions that address high-value business challenges. - Deliver scalable, production-ready models that improve operational efficiency, reliability, productivity, and decision-making. - Strengthen analytical capabilities through robust experimentation, model optimization, and continuous performance improvement. Business Value & Use Case Development - 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. AI Industrialization & Platform Integration - 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. Data Quality & Analytical Foundations - 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. Responsible AI & Governance - 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. Stakeholder Engagement & Value Realization - 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. 3. What Success Looks Like - 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. 4. Minimum Qualifications - Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, Artificial Intelligence, or a related quantitative discipline. 5. Experience - 6–8 years of experience in data science, machine learning, advanced analytics, or AI-related roles. - Exper

Lead the end-to-end development of machine learning, AI, and advanced analytics solutions that address high-value business challenges. Deliver scalable, production-ready models that improve 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.