Lead Specialist Data Science & Analytics II📣 Job Ad

About the Role

Ma'aden, established in 1997, is a rapidly growing global mining company and the largest multi-commodity mining and metals company in the Middle East. This role offers an opportunity to contribute to ambitious growth and shape the future of the mining industry in Saudi Arabia. The Lead Specialist, Data Science & Analytics II will be based in Riyadh and works on a full-time basis.

Job Purpose

The Lead Specialist, Data Science & Analytics serves as a technical leader and senior practitioner, responsible for developing, deploying, and scaling Machine Learning, AI, and advanced analytics solutions across Ma'aden. This role ensures that analytics products are designed, validated, industrialized, governed, and adopted at scale to deliver measurable value across mining, processing, operations, and enterprise functions. The individual will analyze data, extract insights, and build predictive models to support informed decision-making and solve complex challenges, blending expertise in statistics, computer science, and business strategy.

Key Responsibilities

• Lead the end-to-end delivery of data science initiatives, including problem framing, data exploration, feature engineering, model development, validation, and handover.
• Develop, implement, and maintain databases and data collection systems, ensuring data security and regulatory compliance.
• Perform statistical analysis, apply data mining techniques, and build predictive models to forecast future outcomes and identify trends.
• Create clear data visualizations and reports to communicate findings to stakeholders and collaborate with cross-functional teams to provide data-driven solutions.
• Design and maintain reliable data pipelines in partnership with data engineering to ensure data accuracy and timeliness.
• Drive experimentation, model versioning, automated retraining, and continuous improvement of analytics solutions.
• Translate business needs into AI/Analytics solutions by establishing frameworks and operating models for accessible and scalable data science.
• Engage with stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.
• Industrialize AI/ML models by partnering with data engineering, data platforms, and architecture teams for enterprise system integration.
• Set standards for production deployment, testing, monitoring, drift handling, and lifecycle governance of models.
• Ensure compliance with Responsible AI, data quality, and data governance frameworks, promoting transparency and explainability.
• Communicate insights, results, risks, and recommendations to decision-makers and track value realization and operational impact.

Qualifications and Experience

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related field.
• 6+ years of experience in Data Science / Advanced Analytics, with a preference for industrial, mining, or heavy-asset environments.
• At least 2 years of experience leading or mentoring analytics professionals.
• Proven ability to translate business problems into analytic approaches, define hypotheses, design analyses, and synthesize results into clear recommendations.
• Strong proficiency with modern ML frameworks (TensorFlow, PyTorch) and cloud platforms (Azure, AWS), including Microsoft AI Factory.
• Strong technical fluency with modern analytics stacks, data modeling, and SQL.
• Hands-on experience developing and deploying machine learning models for time-series forecasting, predictive modeling, and optimization.
• Practical experience with Generative AI solutions, including copilots, intelligent automation, and agent-based workflows.

Preferred Capabilities

• Experience designing and maintaining data pipelines across IT and OT environments, with exposure to sensor data and streaming processing.
• Experience in model deployment and lifecycle management (MLOps/AgentOps), including transition to production, monitoring, retraining, and versioning.
• Experience working with enterprise cloud platforms such as Microsoft Azure Data Platform and Databricks AI Platform.

Core Competencies

Candidates should demonstrate strong capabilities in:

• Model Accuracy & Reliability: Performance, drift stability, and operational uptime.
• Adoption & Business Impact: Value realized, user adoption, and integration success.
• Delivery Velocity: Timeliness of development cycles and deployment readiness.
• Compliance & Quality: Alignment with Responsible AI, governance, and documentation standards.