Principal Specialist, Data Science & Analytics

Role Overview

Principal Specialist, Data Science & Analytics at Ma'aden. This is a senior technical leadership role driving the development, deployment, and scaling of Machine Learning, AI, and advanced analytics solutions across the organization.

Role Purpose

The Principal Specialist acts as a technical leader and senior practitioner, ensuring analytics products are designed, validated, industrialized, governed, and adopted at scale to provide measurable value across mining, processing, operations, and enterprise functions. The role analyzes data, extracts insights, and builds predictive models that help the organization make smarter decisions and solve difficult problems by blending expertise in statistics, computer science, and business strategy.

Key Responsibilities

End-to-End Data Science Delivery

• Own the full lifecycle of ML/AI initiatives from problem framing, data exploration, feature engineering, model development, validation, and MLOps handover.
• Develop, implement, and maintain databases and data collection systems.
• Perform statistical analysis to understand and interpret data insights.
• Apply data mining techniques to identify patterns, trends, and relationships in large datasets.
• Build predictive models and machine learning algorithms to forecast future outcomes.
• Deliver scalable and production-grade models, ensuring alignment with enterprise data governance and AI standards.
• Design and maintain reliable data pipelines and models in partnership with data engineering to ensure data is accurate, timely, and trustworthy for downstream use.
• Drive experimentation, model versioning, automated retraining, and continuous improvement.
• Ensure data security and compliance with relevant regulations.

Insights & Communication

• Create clear data visualizations and reports to communicate findings to stakeholders.
• Communicate insights, results, risks, and recommendations to decision-makers using compelling narratives and visualization.

Stakeholder Engagement & Value Creation

• Work with cross-functional teams to understand business needs and provide data-driven solutions.
• Engage business unit and domain stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.
• Build value hypotheses, KPIs, success criteria, and solution roadmaps in collaboration with Data & AI leadership and business teams.
• Track value realization, adoption metrics, and operational impact to ensure measurable benefit.
• Establish frameworks and operating models that make data science accessible, scalable, and embedded within business and technical functions.

Industrialization & 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.
• Ensure seamless integration of predictive and optimization models into enterprise platforms, control systems, and digital twins.
• Leverage machine learning, optimization, and computer vision as enabling tools for performance, reliability, and sustainability improvements.

Governance & Quality Assurance

• Ensure compliance with Ma'aden's Responsible AI, data quality, and data governance frameworks.
• Promote reproducibility, documentation, lineage tracking, and auditability across all data science assets.
• Ensure transparency, explainability, and continuous model governance across production and enterprise environments.

Qualifications & Experience

Education

• Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related fields.

Professional Experience

• 8 to 10 years' experience in Data Science or Advanced Analytics, with industrial, mining, or heavy-asset environments preferred.
• At least 2 years leading or mentoring analytics professionals.
• Proven ability to translate business problems into analytic approaches: define hypotheses, design analyses, and synthesize results into clear recommendations.
• Hands-on experience developing and deploying machine learning models, including time-series forecasting, predictive modeling, and optimization use cases.
• Practical experience with Generative AI solutions, including copilots, intelligent automation, and agent-based workflows.
• Ability to embed GenAI capabilities into enterprise processes to improve decision-making and operational efficiency.
• Experience in model deployment and lifecycle management, including transition from model development to production and scale, monitoring, retraining, versioning, and drift management.
• Experience designing and maintaining data pipelines across IT and OT environments.
• Exposure to sensor data, streaming or real-time data processing, and industrial data sources.

Skills & Competencies

Technical Skills

• Strong proficiency with modern ML frameworks and cloud platforms: TensorFlow, PyTorch, Azure, AWS.
• Strong technical fluency with modern analytics stacks, data modeling, and SQL.
• Experience working with enterprise cloud platforms, preferably Microsoft Azure Data Platform, Databricks AI Platform, or Microsoft AI Foundry / Microsoft AI Factory.
• Understanding of cloud-native architectures for scalable analytics and AI solutions.
• Familiarity with automation and operationalization of ML/AI workloads.

Core Competencies

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

Interpersonal Skills

• Ability to collaborate effectively with engineering teams.
• Ability to collaborate with data engineering teams to ensure reliable, timely, and trusted data flows.