Principal Specialist, Data Science & Analytics

Job Purpose

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 Maaden. The role ensures analytics products are designed, validated, industrialized, governed, and adopted at scale, providing measurable value across mining, processing, operations, and enterprise functions. The Specialist analyzes data, extracts insights, and builds predictive models that help organizations make smarter decisions and solve difficult problems.

• Develop, implement, and maintain databases and data collection systems.
• Own the full lifecycle of ML/AI initiatives—from problem framing, data exploration, feature engineering, model development, validation, to MLOps handover.
• Deliver scalable and production‑grade models, ensuring alignment with enterprise data governance and AI standards.
• 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.
• Create clear data visualizations and reports to communicate findings to stakeholders.
• Work with cross‑functional teams to understand business needs and provide data‑driven solutions.
• Design and maintain reliable data pipelines and models in partnership with data engineering to ensure data is accurate, timely, and trustworthy for downstream use.
• Ensure data security and compliance with relevant regulations.
• Drive experimentation, model versioning, automated retraining, and continuous improvement.

• 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.
• Build value hypotheses, KPIs, success criteria, and solution roadmaps in collaboration with Data & AI leadership and business teams.

• 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.

• Ensure compliance with Maaden’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.

• 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.

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

• 8–10 years experience in Data Science / Advanced Analytics with industrial, mining, or heavy‑asset environments preferred, including 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.
• Strong proficiency with modern ML frameworks and cloud platforms (Tensor Flow, PyTorch, Azure, AWS, Microsoft AI Factory).
• Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams.
• Machine…