Principal Specialist Data Science & Analytics📣 Job Ad

About Ma'aden

Ma'aden, established in 1997, is one of the fastest-growing mining companies globally and the largest multi-commodity mining and metals company in the Middle East. The company leads the development of the mining industry to become the third pillar of Saudi Arabia’s economy by building a world-class, fully integrated mining value chain.

Role Overview

Ma'aden is seeking a Principal Specialist, Data Science & Analytics to join its team in Riyadh. This full-time role serves as a technical leader and senior practitioner, responsible for driving the development, deployment, and scaling of Machine Learning, AI, and advanced analytics solutions across the organization. The specialist ensures analytics products are designed, validated, industrialized, governed, and adopted at scale, providing measurable value across mining, processing, operations, and enterprise functions. This position requires 5-10 years of experience.

Key Responsibilities

This role involves leading end-to-end data science delivery and translating business needs into AI/analytics solutions. Key responsibilities include:

• Developing, implementing, and maintaining databases and data collection systems.
• Owning the full lifecycle of ML/AI initiatives – from problem framing, data exploration, feature engineering, model development, validation, and MLOps handover.
• Delivering scalable and production-grade models, ensuring alignment with enterprise data governance and AI standards.
• Performing statistical analysis and applying data mining techniques to identify patterns, trends, and relationships in large datasets.
• Building predictive models and machine learning algorithms to forecast future outcomes.
• Creating clear data visualizations and reports to communicate findings to stakeholders.
• Working with cross-functional teams to understand business needs and provide data-driven solutions.
• Designing and maintaining reliable data pipelines and models in partnership with data engineering.
• Ensuring data security and compliance with relevant regulations, and driving experimentation, model versioning, automated retraining, and continuous improvement.
• Establishing frameworks and operating models to make data science accessible, scalable, and embedded within business and technical functions.
• Engaging business unit and domain stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.
• Building value hypotheses, KPIs, success criteria, and solution roadmaps in collaboration with Data & AI leadership and business teams.

Model Industrialization and Governance

The Principal Specialist will be responsible for industrializing AI/ML models and ensuring responsible AI practices. This includes:

• Partnering with data engineering, data platforms, and cloud/OT architecture teams to embed models into enterprise systems and operational layers.
• Setting standards for production deployment, testing, monitoring, drift handling, and lifecycle governance.
• Ensuring seamless integration of predictive and optimization models into enterprise platforms, control systems, and digital twins.
• Leveraging machine learning, optimization, and computer vision as enabling tools for performance, reliability, and sustainability improvements.
• Ensuring compliance with Ma'aden’s Responsible AI, data quality, and data governance frameworks.
• Promoting reproducibility, documentation, lineage tracking, and auditability across all data science assets.
• Ensuring transparency, explainability, and continuous model governance across production and enterprise environments.

Stakeholder Management and Value Realization

This role requires effective communication and tracking of impact to ensure business value. Key aspects include:

• Communicating insights, results, risks, and recommendations to decision-makers using compelling narratives and visualization.
• Tracking value realization, adoption metrics, and operational impact to ensure measurable benefit.

Core Competencies

The successful candidate will demonstrate competencies in:

• Model Accuracy & Reliability: Measured by performance, drift stability, and operational uptime.
• Adoption & Business Impact: Assessed by value realized, user adoption, and integration success.
• Delivery Velocity: Reflecting the timeliness of development cycles and deployment readiness.
• Compliance & Quality: Adherence to Responsible AI, governance, and documentation standards.