Principal Specialist Data Science & Artificial Intelligence

About Ma'aden

Ma'aden, established in 1997, is a global mining company and the largest multi-commodity mining and metals company in the Middle East. The company is focused on developing the mining industry to become a key economic pillar in Saudi Arabia by building an integrated mining value chain. This full-time role is based in Riyadh.

Role Overview

The Principal Specialist, Data Science & Artificial Intelligence, will lead advanced data science and analytics initiatives within the Agentic AI department. This role involves translating business problems into scalable analytical solutions, AI-enabled insights, predictive models, and decision intelligence products. The specialist will contribute to building enterprise capabilities that leverage trusted data, machine learning, automation, and agentic AI concepts to enhance productivity, operational performance, and business decision-making.

Key Responsibilities

• Translate business priorities, operational challenges, and use cases into clear data science problem statements, analytical approaches, and measurable outcomes.
• Design and deliver predictive, prescriptive, diagnostic, and exploratory analytics solutions that support decision-making across business and operational domains.
• Develop machine learning models, statistical analyses, simulations, optimization approaches, and advanced analytics products using enterprise data assets.
• Support the development of agentic AI use cases by defining data, model, workflow, evaluation, and decision-support requirements.
• Collaborate with AI engineers, platform teams, data engineers, and business stakeholders to embed analytics and machine learning into autonomous or semi-autonomous workflows.
• Create reusable analytical assets, data science patterns, feature logic, model components, and decision intelligence products that can scale across business domains.
• Apply appropriate data science governance practices for model validation, performance monitoring, bias checks, versioning, deployment readiness, and lifecycle management.
• Provide expert guidance to analysts, data scientists, engineers, and business teams on analytical methods, model design, evaluation, and adoption.
• Advise stakeholders on what analytics or AI can realistically solve, required data readiness, implementation complexity, and expected value.
• Communicate insights, risks, limitations, and recommended actions clearly to technical, operational, and executive stakeholders.

Qualifications and Experience

• A minimum of 10 years of relevant experience in data science and artificial intelligence.
• Demonstrated ability to ensure analytical outputs are interpretable, actionable, repeatable, and aligned with business value, adoption needs, and governance requirements.
• Experience in defining evaluation approaches for AI-enabled insights, model performance, business impact, human oversight, and continuous improvement.
• Proficiency in integrating analytics into dashboards, workflows, applications, copilots, and AI agents for improved decision support.
• Ability to maintain clear documentation covering assumptions, data sources, model logic, limitations, controls, and expected business usage.
• Experience in monitoring solution performance after deployment and recommending improvements based on usage, feedback, model drift, data quality, and business impact.

Success Factors

Successful candidates will effectively translate business problems into clear data science use cases and analytical approaches with measurable value outcomes. Success is also measured by the delivery of predictive, prescriptive, and decision intelligence solutions that demonstrate strong technical quality and practical business relevance. This includes supporting Agentic AI use cases with trusted data, robust models, evaluation methods, controls, and human oversight, ensuring analytics and AI products are reusable, documented, governed, monitored, and aligned with responsible AI expectations. Ultimately, success involves providing stakeholders with clear insights, recommendations, and decision support that improve productivity, performance, and adoption.