Principal Specialist, Data Science & Artificial Intelligence

Ma'aden Aluminium Company (MAC); Medina; On-site; SAR 400,000 - 700,000; Full time

Job summary

Ma'aden Aluminium Company in Medina seeks a lead Data Science & Analytics professional in the Agentic AI domain to translate business problems into scalable analytical solutions, develop AI-enabled insights, predictive models, and decision intelligence products.

You will collaborate with AI engineers, data engineers, and business stakeholders to embed analytics into autonomous workflows while ensuring transparency, governance, and measurable business impact.

Job description

Why This Role Matters

This role leads advanced data science and analytics initiatives within the Agentic AI department translating business problems into scalable analytical solutions AI-enabled insights predictive models and decision intelligence products It helps build enterprise capabilities that use trusted data machine learning automation and agentic AI concepts to improve productivity operational performance and business decision-making

What You Will Deliver

• Lead Data Science and Analytics Solutions


• 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


• Ensure analytical outputs are interpretable actionable repeatable and aligned with business value adoption needs and governance requirements


• Enable Agentic AI and Intelligent Analytics 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


• Advance Data Products and Decision Intelligence 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


• Ensure Model Quality Governance and Value Realization 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 impactunable to parse proportionally