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. Why This Role Matters Disciplined delivery management converts Data & AI priorities into reliable, scalable, and measurable business outcomes. The Lead Specialist manages integrated delivery across business, technology, and vendor workstreams. The role strengthens execution discipline, removes delivery barriers, and guides priority initiatives from approved scope through sustainable adoption and value realization. What You Will Deliver Lead Initiative Delivery - Translate approved objectives and scope into integrated delivery plans. - Define milestones, deliverables, dependencies and accountabilities. - Lead delivery cadence across business, technology and vendor workstreams. - Manage initiatives using Agile, waterfall, or hybrid delivery approaches, including sprint planning, backlog coordination, execution tracking, reviews, and retrospectives. - Monitor schedule, scope, quality, cost, risks and issues. Strengthen Delivery Governance - Apply approved delivery standards, stage gates and reporting requirements. - Prepare decision materials and recommendations for governance forums. - Maintain records of plans, decisions, acceptance evidence and closure. Drive Cross-Functional Execution - Align stakeholders on outcomes, priorities, resources and handoffs. - Facilitate resolution of delivery blockers and inter-team dependencies. - Coordinate required finance, procurement, architecture and technology inputs. - Escalate material delivery exceptions with clear recommendations. Enable Value Realization - Define measurable outcomes and track realization through delivery and adoption. - Support transition from pilot to production and enterprise scale-up. Improve Delivery Capability - Capture lessons learned and delivery-performance insights. - Improve delivery methods, templates and ways of working. - Coach delivery contributors on consistent governance and execution practices. What Success Looks Like - Initiatives delivered against agreed scope, milestones and quality. - Early identification and resolution of delivery risks and dependencies. - Clear alignment across business, technology and vendor stakeholders. - Reliable governance, reporting and decision follow-through. - Effective transition into adoption, operations and value realization. - Consistent and repeatable delivery practices across the portfolio. Minimum Qualifications - Bachelor’s degree in engineering, computer science, information systems, business, or related discipline. - Project Management Professional (PMP) certification is required. Experience - 7–10 years of experience in project, program or technology delivery, including complex cross-functional initiatives. - Experience delivering data, analytics, AI or digital initiatives and coordinating vendors is preferred. Skills That Matter - Program and project delivery - Integrated planning and scheduling - Agile and hybrid delivery methods - Risk and issue management - Dependency management - Governance and change control - Stakeholder management - Vendor coordination - Value realization and adoption - Executive reporting - Facilitation and negotiation - Problem solving and decision support Benefits 1. Career Growth 2. Competitive Salaries 3. Comprehensive Package 4. Inclusive Work Environment 5. Work-Life Balance 6. Employee wellness

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.