Lead Specialist, Data Science & Analytics II

The Lead Specialist, Data Science & Analytics, acts as a technical leader and senior practitioner, driving development, deployment, and scaling of Machine Learning, AI, and advanced analytics solutions across the company. The role ensures analytics products are designed, validated, industrialized, governed, and adopted at scale, providing measurable value across mining, processing, operations, and enterprise functions. This position analyzes data, extracts insights, and builds predictive models that help organizations make smarter decisions and solve difficult problems.

Location: Riyadh, Saudi Arabia
Work Conditions: Full time

Key Accountabilities

1. Lead End-to-End Data Science Delivery

   • Developing, implementing, and maintaining databases and data collection systems
   • Own the full lifecycle of ML/AI initiatives - from problem framing, data exploration, feature engineering, model development, validation, and MLOps handover.
   • Deliver scalable and production-grade models, ensuring alignment with enterprise data governance and AI standards.
   • Performing statistical analysis to understand and interpret data insights.
   • Building predictive models and machine learning algorithms to forecast future outcomes.
   • Creating clear data visualizations and reports to communicate findings to stakeholders.

2. Translate Business Needs into AI/Analytics Solutions

   • Establish frameworks and operating models that make data science accessible, scalable, and embedded within business and technical functions.
   • Engage BU/domain stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.

3. Industrialize AI/ML Models (ML Ops & Architecture)

   • Partner with data engineering, data platforms, and cloud/OT architecture teams to embed models into enterprise systems and operational layers.
   • Set standards for production deployment, testing, monitoring, drift handling, and lifecycle governance.

4. Responsible AI, Quality & Governance

   • Ensure compliance with the company’s Responsible AI, data quality, and data governance frameworks.
   • Promote reproducibility, documentation, lineage tracking, and auditability across all data science assets.

5. Stakeholder Management & Value Realization

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

Requirements

• Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related fields.
• 6+ years’ experience in Data Science / Advanced Analytics with industrial, mining, or heavy-asset environments preferred.
• Proven ability to translate business problems into analytic approaches.
• Strong proficiency with modern ML frameworks and cloud platforms (e.g., TensorFlow, PyTorch, Azure, AWS).
• Hands-on experience developing and deploying machine learning models, including time-series forecasting and predictive modeling.

Core Competencies

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

Benefits

• Career Growth
• Competitive Salaries
• Comprehensive Package
• Inclusive Work Environment
• Work-Life Balance
• Employee wellness