Lead Specialist, Data Science & Artificial Intelligence

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 The Senior Specialist, Data Science & Artificial Intelligence II accelerates business value creation through the application of advanced analytics, machine learning, and Generative AI solutions. The role transforms data into actionable intelligence that improves decision-making, operational performance, automation, innovation, and business outcomes across the enterprise. The role enables Ma’aden to harness the power of AI by developing scalable, reliable, and responsible solutions that address complex business challenges. Through the deployment of machine learning models and GenAI applications, the role improves efficiency, enhances user experiences, and unlocks new opportunities for digital transformation. By combining technical expertise with governance, security, and ethical AI practices, the role ensures that AI solutions deliver measurable value while maintaining trust, compliance, and sustainable adoption across the organization. What You Will Deliver AI Solution Development & Innovation - Develop and deploy machine learning, deep learning, and Generative AI solutions that address strategic and operational business challenges. - Design scalable AI applications that improve decision-making, productivity, automation, and business performance. - Accelerate AI adoption through innovative use of advanced analytics and emerging AI technologies. Model Performance & Optimization - Improve model accuracy, reliability, and effectiveness through feature engineering, tuning, evaluation, and optimization techniques. - Strengthen AI outcomes through robust testing, validation, and continuous performance enhancement. - Ensure AI solutions remain aligned with business objectives and evolving operational requirements. Generative AI & LLM Enablement - Build and enhance GenAI applications using prompt engineering, retrieval-augmented generation (RAG), and large language model technologies. - Improve response quality, accuracy, and relevance through structured evaluation and optimization approaches. - Deliver enterprise-ready AI capabilities that support knowledge discovery, content generation, and intelligent automation. Data Preparation & Engineering Enablement - Transform structured and unstructured data into high-quality datasets suitable for AI and machine learning applications. - Improve data usability and reliability through effective cleansing, preparation, and feature development practices. - Ensure AI solutions are built on trusted, governed, and business-relevant data assets. AI Operations & Lifecycle Management - Support deployment, monitoring, retraining, and lifecycle management of AI solutions using MLOps and LLMOps practices. - Improve operational reliability and scalability of production AI models and applications. - Enable sustainable AI adoption through effective performance monitoring and continuous improvement. Governance, Risk & Responsible AI - Ensure AI solutions comply with enterprise governance, cybersecurity, privacy, and ethical AI requirements. - Strengthen transparency and trust by documenting models, assumptions, risks, and validation outcomes. - Promote responsible AI practices that balance innovation with risk management and compliance obligations. What Success Looks Like - AI and machine learning solutions deliver measurable business value and operational improvement. - Generative AI applications provide accurate, reliable, and high-quality outputs for end users. - Machine learning models achieve performance targets and remain effective throughout their lifecycle. - AI solutions are successfully integrated into enterprise processes and systems. - MLOps and LLMOps practices improve model reliability, scalability, and operational efficiency. - Governance, security, and responsible AI requirements are consistently embedded within AI initiatives. Minimum Qualifications - Bachelor’s degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative discipline. Experience - 4–6 years of experience in data science, machine learning, artificial intelligence, or advanced analytics roles. - Experience developing and deploying machine learning and AI solutions in business environments. - Experience working with structured and unstructured data for analytical and AI use cases. - Experience supporting AI solution deployment, monitoring, and model l