Lead Specialist Data Science & Artificial Intelligence

About Ma'aden

Ma'aden, established in 1997, is recognized as one of the fastest-growing mining companies globally and the largest multi-commodity mining and metals company in the Middle East. The company is instrumental in developing the mining industry to establish it as the third pillar of Saudi Arabia’s economy, building a fully integrated mining value chain.

Role Overview: Lead Specialist, Data Science & Artificial Intelligence

This full-time role is based in Riyadh and involves leading advanced data science and analytics initiatives within the Agentic AI department. The Lead Specialist will translate business problems into scalable analytical solutions, AI-enabled insights, predictive models, and decision intelligence products. The position focuses on 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 to 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.
• Ensure analytical outputs are interpretable, actionable, repeatable, and aligned with business value, adoption needs, and governance requirements.
• 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.
• Define evaluation approaches for AI-enabled insights, model performance, business impact, human oversight, and continuous improvement.
• Promote responsible use of agentic AI by ensuring transparency, explainability, control, auditability, and fit-for-purpose model deployment.
• Create reusable analytical assets, data science patterns, feature logic, model components, and decision intelligence products that can scale across business domains.
• Partner with data platform, governance, and architecture teams to ensure data products are trusted, well-documented, reusable, and operationally maintainable.
• Integrate analytics into dashboards, workflows, applications, copilots, and AI agents where decision support can improve speed, quality, and productivity.
• Maintain clear documentation covering assumptions, data sources, model logic, limitations, controls, and expected business usage.
• Apply appropriate data science governance practices for model validation, performance monitoring, bias checks, versioning, deployment readiness, and lifecycle management.
• Define success measures, impact metrics, adoption criteria, and benefit tracking approaches for analytics and AI-enabled solutions.
• Monitor solution performance after deployment and recommend improvements based on usage, feedback, model drift, data quality, and business impact.
• Ensure data science and analytics solutions remain compliant with data governance, cybersecurity, privacy, and responsible AI expectations.
• 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.
• Review analytical designs, model outputs, assumptions, and recommendations to ensure technical quality and business relevance.
• 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.

Performance Indicators

• Business problems are translated into clear data science use cases, analytical approaches, and measurable value outcomes.
• Predictive, prescriptive, and decision intelligence solutions are delivered with strong technical quality and practical business relevance.
• Agentic AI use cases are supported with trusted data, robust models, evaluation methods, controls, and human oversight where required.
• Analytics and AI products are reusable, documented, governed, monitored, and aligned with responsible AI expectations.
• Stakeholders receive clear insights, recommendations, and decision support that improve productivity, performance, and adoption.