Lead Specialist Data & Artificial Intelligence Delivery Management

📣 إعلان

منذ ساعة واحدة تقريباً

	نوع العقد	دوام كامل
	طبيعة الوظيفة	بالموقع
	الموقع	السعودية

وصف الوظيفة

About the Role

Maaden is seeking a Lead Specialist for Data & Artificial Intelligence Delivery Management in Saudi Arabia. This full-time role focuses on driving the development, deployment, and adoption of advanced analytics, machine learning, and AI solutions to enhance operational performance, business decision-making, and enterprise productivity across the organization. The position involves transforming complex business challenges into scalable analytical solutions that generate measurable value across various functions, including mining, processing, operations, and corporate.

Role Purpose and Impact

The Lead Specialist 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, this role ensures analytical solutions are reliable, scalable, compliant, and aligned with enterprise standards, enabling sustainable adoption and value realization across the organization.

Key Responsibilities

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

Qualifications and Experience

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

Success Metrics

Success in this role is measured by the delivery of machine learning and AI solutions that yield measurable operational, financial, and productivity improvements. This includes the successful adoption and sustained use of analytical products across targeted business functions, with models achieving required standards for performance, reliability, scalability, and business relevance. Efficient progression of AI and analytics projects from concept to production deployment, alongside compliance with governance, security, and Responsible AI requirements, are also key indicators. Ultimately, success involves enabling business stakeholders to make faster and better decisions through trusted analytical insights and predictive capabilities.