Lead Specialist Data & Artificial Intelligence Delivery Management

About Ma'aden

Ma'aden, established in 1997, is a multi-commodity mining and metals company based in the Middle East. The company contributes to the development of the mining industry in Saudi Arabia, aiming to establish a fully integrated mining value chain. This full-time role is based in Riyadh.

Role Overview

The Lead Specialist, Data & Artificial Intelligence Delivery Management, drives the development, deployment, and adoption of advanced analytics, machine learning, and AI solutions across Ma'aden. This role focuses on improving operational performance, business decision-making, and enterprise productivity. It involves transforming complex business challenges into scalable analytical solutions that generate measurable value across various functions, including mining, processing, operations, and corporate. The specialist will bridge business priorities with technical execution, ensuring analytical products progress efficiently from concept through deployment and industrialization. The role also involves embedding AI and data science capabilities into operational processes to accelerate digital transformation and strengthen Ma'aden’s ability to leverage data as a strategic asset. Through technical leadership, governance oversight, and stakeholder engagement, this position ensures analytical solutions are reliable, scalable, compliant, and aligned with enterprise standards.

Key Responsibilities

• Lead the end-to-end development of machine learning, AI, and advanced analytics solutions for business challenges.
• Deliver scalable, production-ready models to 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 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 partnerships with business, technology, and Data & AI stakeholders to accelerate solution adoption.
• Communicate analytical insights, risks, recommendations, and outcomes through narratives and visualizations.
• Measure and improve business impact through continuous tracking of adoption, performance, and value realization.

Qualifications and Experience

• 6–8 years of experience in data science, machine learning, advanced analytics, or AI-related roles.
• Experience developing and deploying production-grade analytical and machine learning solutions.
• Experience translating business problems into analytical frameworks, predictive models, and actionable recommendations.
• Experience working with cross-functional business, technology, and data teams in complex organizational environments.

Performance Indicators

• 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.