Lead Specialist, Data & Artificial Intelligence Delivery Management

Ma'aden is hiring a Lead Specialist, Data & Artificial Intelligence Delivery Management in Riyadh, Saudi Arabia. Level rates it ; you can apply on Level.

AI in this role

Drive the end-to-end development, deployment, and adoption of AI and machine learning solutions to optimize mining and business operations.

pythonmachine-learning

ml-opsai-safetydata-scienceanalyticsmachine-learningsolution-deliverystakeholder-engagement

Maaden, established in 1997, is one of the fastest-growing mining companies in the world and the largest multi-commodity mining and metals company in the Middle East. We are leading the development of the mining industry to become the third pillar of Saudi Arabia’s economy by building a world-class, unique, and fully integrated mining value chain. We are pleased to share an exciting opportunity for the below mentioned position. This role offers a chance to contribute to our ambitious growth and play a key part in shaping the future of mining in the Kingdom.

1. Why This Role Matters The Lead Specialist, Data Science & Analytics drives the development, deployment, and adoption of advanced analytics, machine learning, and AI solutions that improve operational performance, business decision-making, and enterprise productivity across Maaden. The role transforms complex business challenges into scalable analytical solutions that generate measurable value across mining, processing, operations, and corporate functions. The role bridges business priorities with technical execution, ensuring that analytical products progress efficiently from concept through deployment and industrialization. By embedding AI and data science capabilities into operational processes, the role accelerates digital transformation and strengthens Maaden’s ability to leverage data as a strategic asset. Through technical leadership, governance oversight, and stakeholder engagement, the role ensures analytical solutions are reliable, scalable, compliant, and aligned with enterprise standards, enabling sustainable adoption and value realization across the organization. 2. What You Will Deliver AI & Data Science Solution Delivery

Lead the end-to-end development of machine learning, AI, and advanced analytics solutions that address high-value business challenges.
Deliver scalable, production-ready models that improve operational efficiency, reliability, productivity, and decision-making.
Strengthen analytical capabilities through robust experimentation, model optimization, and continuous performance improvement.

Business Value & Use Case Development

Translate business priorities into actionable analytics opportunities, value hypotheses, and implementation roadmaps.
Identify, prioritize, and develop AI and analytics use cases that deliver measurable operational and financial outcomes.
Establish clear success measures, KPIs, and value realization frameworks for analytical initiatives.

AI Industrialization & Platform Integration

Enable seamless deployment of analytical models into enterprise systems, operational platforms, and digital environments.
Strengthen model reliability and scalability through effective MLOps practices, lifecycle management, and monitoring standards.
Improve adoption and sustainability of AI solutions by ensuring technical readiness and operational integration.

Data Quality & Analytical Foundations

Strengthen trust in analytics through reliable, governed, and high-quality data foundations.
Partner with Data Engineering teams to improve analytical environments, data pipelines, and model readiness.
Ensure data assets meet enterprise requirements for accuracy, integrity, security, and compliance.

Responsible AI & Governance

Embed responsible AI principles, governance controls, and quality standards across analytical solutions.
Strengthen transparency, explainability, auditability, and lineage across data science assets and models.
Ensure compliance with enterprise data governance, security, and regulatory requirements.

Stakeholder Engagement & Value Realization

Build strong partnerships with business, technology, and Data & AI stakeholders to accelerate solution adoption.
Communicate analytical insights, risks, recommendations, and outcomes through compelling narratives and visualizations.
Measure and improve business impact through continuous tracking of adoption, performance, and value realization.

3. What Success Looks Like

Machine learning and AI solutions deliver measurable operational, financial, and productivity improvements.
Analytical products are successfully adopted and sustained across targeted business functions.
Models achieve required standards for performance, reliability, scalability, and business relevance.
AI and analytics projects progress efficiently from concept to production deployment.
Data science initiatives demonstrate compliance with governance, security, and Responsible AI requirements.
Business stakeholders make faster and better decisions through trusted analytical insights and predictive capabilities.

4. Minimum Qualifications

Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, Artificial Intelligence, or a related quantitative discipline.

5. Experience

6–8 years of experience in data science, machine learning, advanced analytics, or AI-related roles.
Experience developing and deploying production-grade analytical and machine learning solutions.
Experience translating business problems into analytical frameworks, predictive models, and actionable recommendations.
Experience working with cross-functional business, technology, and data teams in complex organizational environments.

6. Skills That Matter Functional Expertise

Machine Learning & Artificial Intelligence
Predictive Analytics & Statistical Modeling
Data Mining & Pattern Discovery
Feature Engineering & Model Optimization
Data Visualization & Analytical Storytelling
AI Solution Design & Industrialization

Business & Delivery

Python, SQL & Advanced Analytics Tools
MLOps & Model Lifecycle Management
Data Pipeline & Analytical Environment Design
AI Product Delivery & Value Realization
Data Governance & Compliance
Performance Measurement & KPI Development

People & Collaboration

Stakeholder Management
Business & Technical Translation
Cross-Functional Collaboration
Communication & Presentation Skills
Problem Solving & Critical Thinking
Continuous Improvement & Accountability

Benefits

1. Career Growth
2. Competitive Salaries
3. Comprehensive Package
4. Inclusive Work Environment
5. Work-Life Balance
6. Employee wellness

How we rate this

Lead Specialist, Data & Artificial Intelligence Delivery Management at Ma'aden rates 80 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Lead Specialist, Data & Artificial Intelligence Delivery Management is an open job at Ma'aden.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Riyadh, Saudi Arabia.
The job was posted on October 14, 2026.
Seniority: lead.
Applications are handled by Ma'aden. Apply on Ma'aden

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsAI SafetyData ScienceAnalyticsMachine learningSolution DeliveryStakeholder EngagementPython

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
How do you think about the risk of an AI system in this kind of role failing silently?
Tell me about a project where data science was part of your work. What did you do?
Tell me about a project where analytics was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: ML Ops, AI Safety, Data Science, Analytics, and Machine learning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

Want an expert to read your CV for this job?

Free. Send your CV and the role you want next. We reply by email within 2 to 4 business days.

Get a free CV review Check your resume with the ATS resume checker