168 وظيفة Ai Advanced Analytic Specialist في Riyadh

Job Description

Why This Role Matters

The Senior Specialist, Data Science & Artificial Intelligence II accelerates business value creation through the application of advanced analytics, machine learning, and Generative AI solutions. The role transforms data into actionable intelligence that improves decision-making, operational performance, automation, innovation, and business outcomes across the enterprise.

The role enables Ma’aden to harness the power of AI by developing scalable, reliable, and responsible solutions that address complex business challenges. Through the deployment of machine learning models and GenAI applications, the role improves efficiency, enhances user experiences, and unlocks new opportunities for digital transformation.

By combining technical expertise with governance, security, and ethical AI practices, the role ensures that AI solutions deliver measurable value while maintaining trust, compliance, and sustainable adoption across the organization.

What You Will Deliver

AI Solution Development & Innovation

• Develop and deploy machine learning, deep learning, and Generative AI solutions that address strategic and operational business challenges.
• Design scalable AI applications that improve decision-making, productivity, automation, and business performance.
• Accelerate AI adoption through innovative use of advanced analytics and emerging AI technologies.
  
  

• Improve model accuracy, reliability, and effectiveness through feature engineering, tuning, evaluation, and optimization techniques.
• Strengthen AI outcomes through robust testing, validation, and continuous performance enhancement.
• Ensure AI solutions remain aligned with business objectives and evolving operational requirements.
  
  

• Build and enhance GenAI applications using prompt engineering, retrieval-augmented generation (RAG), and large language model technologies.
• Improve response quality, accuracy, and relevance through structured evaluation and optimization approaches.
• Deliver enterprise-ready AI capabilities that support knowledge discovery, content generation, and intelligent automation.
  
  

• Transform structured and unstructured data into high-quality datasets suitable for AI and machine learning applications.
• Improve data usability and reliability through effective cleansing, preparation, and feature development practices.
• Ensure AI solutions are built on trusted, governed, and business-relevant data assets.
  
  

• Support deployment, monitoring, retraining, and lifecycle management of AI solutions using MLOps and LLMOps practices.
• Improve operational reliability and scalability of production AI models and applications.
• Enable sustainable AI adoption through effective performance monitoring and continuous improvement.
  
  

• Ensure AI solutions comply with enterprise governance, cybersecurity, privacy, and ethical AI requirements.
• Strengthen transparency and trust by documenting models, assumptions, risks, and validation outcomes.
• Promote responsible AI practices that balance innovation with risk management and compliance obligations.
  
  

• AI and machine learning solutions deliver measurable business value and operational improvement.
• Generative AI applications provide accurate, reliable, and high-quality outputs for end users.
• Machine learning models achieve performance targets and remain effective throughout their lifecycle.
• AI solutions are successfully integrated into enterprise processes and systems.
• MLOps and LLMOps practices improve model reliability, scalability, and operational efficiency.
• Governance, security, and responsible AI requirements are consistently embedded within AI initiatives.
  
  

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

• 4–6 years of experience in data science, machine learning, artificial intelligence, or advanced analytics roles.
• Experience developing and deploying machine learning and AI solutions in business environments.
• Experience working with structured and unstructured data for analytical and AI use cases.
• Experience supporting AI solution deployment, monitoring, and model lifecycle management.
  
  

• Machine Learning & Deep Learning
• Generative AI & Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Model Fine-Tuning & Evaluation
• Data Preparation & Feature Engineering
  
  

• Python & SQL
• TensorFlow, PyTorch & Scikit-Learn
• MLOps & LLMOps Practices
• API Integration & AI Deployment
• Model Monitoring & Performance Optimization
• Analytical Problem Solving
  
  

• Collaboration & Teamwork
• Stakeholder Engagement
• Communication & Knowledge Sharing
• Results Orientation
• Accountability
• Continuous Improvement