Manager, Data Science & Analytics

Role Purpose:

The Data Science & Analytics Manager is responsible for overseeing the end-to-end execution of data science and advanced analytics initiatives that support Maaden’s operational and business objectives. The role ensures that data science projects are delivered with high quality, reliability, and measurable impact by managing workflows, coordinating cross-functional stakeholders, and maintaining strong alignment with enterprise data, AI, and governance standards.

As an operational leader, the Manager translates business needs into actionable analytical work, supervises the development of models, analytical products, and data pipelines, and ensures their successful deployment and adoption across functions. This role plays a critical part in embedding data-driven decision-making within production, maintenance, supply chain, engineering, and other business units by enabling teams to access trusted insights, robust analytical tools, and fit-for-purpose ML/AI solutions.

Through effective project management, quality oversight, and close collaboration with technical experts and business partners, the Data Science & Analytics Manager helps accelerate Maaden’s transition to a technology-enabled, AI-informed mining enterprise.

1. Manage End‑to‑End Data Science Delivery

• Oversee the development, implementation, and maintenance of databases and data collection systems.

• Manage the lifecycle of ML/AI initiatives—supporting problem framing, data exploration, feature engineering, model development, validation, and preparation for MLOps deployment.

• Ensure delivery of scalable, production‑ready models in alignment with enterprise data governance and AI standards.

• Conduct statistical analysis to interpret data insights and support decision‑making.

• Apply data mining techniques to uncover patterns, trends, and relationships in large datasets.

• Develop predictive models and machine learning algorithms to forecast future outcomes as required.

• Create clear data visualizations, dashboards, and reports to effectively communicate findings to business stakeholders.

• Collaborate with cross‑functional teams to understand business needs and translate them into data‑driven solutions.

• Ensure seamless integration of predictive and optimization models into enterprise platforms, control systems, and digital twins.

• Stay updated on emerging techniques, tools, and technologies in data science and analytics.

• Promote experimentation, model versioning, automated retraining, and continuous improvement processes.

• Recommend enhancements to existing models and analytics workflows for greater efficiency and impact.

2. Data Operations

• Partner with data engineering to design, operate, and maintain reliable data pipelines and analytical models, ensuring data accuracy, timeliness, and trustworthiness.

• Monitor data quality, resolve data issues, and enforce data best practices across teams.

• Support compliance with data security standards and relevant regulations

• Supervise the development and deployment of advanced ML models, predictive analytics, GenAI, and optimization solutions, consistent with responsibilities of analytics leadership roles. 

• Ensure technical rigor across the end‑to‑end ML lifecycle: scoping → data engineering → modeling → validation → MLOps → monitoring.

• Oversee production reliability, model drift management, and continuous improvement.

3. Team Leadership & Capability Building

• Manage and grow a multi‑disciplinary team (Lead Specialists, Senior Specialists, Data Scientists). Roles under this manager are explicitly listed in the org structure. 

• Build technical excellence through coaching on ML, GenAI, statistics, feature engineering, and experimentation.

• Foster a culture of innovation, peer review, and reusable modeling frameworks.

4. AI Governance, Quality & Standards

• Steward high standards in model explainability, auditability, lineage, and risk management.

• Drive compliance with enterprise data governance, PDPL, and model‑risk principles referenced in internal DS/AI qualification guidance.

• Ensure adherence to Responsible AI requirements applicable to DS/AI roles (skills and qualifications guidance).

• Enforce data quality standards, lineage, and reproducibility across the team’s projects.

• Support alignment with enterprise governance frameworks, including PDPL compliance.