Lead Specialist, Data Science & Analytics II at Ma'aden

Riyadh, Riyadh Region, Saudi Arabia -

Full Time

Start Date

Immediate

Expiry Date

25 Jul, 26

Salary

0.0

Posted On

26 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Artificial Intelligence, Data Science, Advanced Analytics, Statistical Analysis, Data Mining, Predictive Modeling, Data Engineering, MLOps, Cloud Platforms, TensorFlow, PyTorch, SQL, Data Governance, Computer Vision, Business Strategy

Industry

mining

Description

Role Purpose: 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 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. Lead Specialist, Data Science & Analytics is to analyze data, extract insights, and build predictive models that help organizations make smarter decisions and solve difficult problems. By blending expertise in statistics, computer science, and business strategy, they not only analyze complex datasets but also build predictive models that improve operations and shape long-term decisions. With nearly every industry leaning on data today, the demand for skilled professionals continues to grow. 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 * Applying data mining techniques to identify patterns, trends, and relationships in large datasets * Building predictive models and machine learning algorithms to forecast future outcomes * Creating clear data visualizations and reports to communicate findings to stakeholders * Working 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 * Ensuring data security and compliance with relevant regulations * Staying up to date with latest technological trends and techniques in data science * Drive experimentation, model versioning, automated retraining, and continuous improvement. 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. * Build value hypotheses, KPIs, success criteria, and solution roadmaps in collaboration with Data & AI leadership and business teams. 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. * 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 4. Responsible AI, Quality & Governance * 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 Minimum Qualifications: Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related fields. Minimum Experience: 5 – 8 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 (TensorFlow, PyTorch, Azure, AWS) Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams. Maaden High-Performance Competencies: 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. Skills: * Machine Learning & AI Leadership * Programming Expertise * Generative AI Proficiency * Strong Analytical & Statistical Skills * Business Strategy & Product Thinking * Ethics, Governance & Responsible AI

Responsibilities

The Lead Specialist will drive the end-to-end lifecycle of ML and AI initiatives, from problem framing and model development to industrialization and MLOps. They will also partner with cross-functional teams to translate business needs into scalable, data-driven analytics solutions.