Senior Specialist, Data Science & Artificial Intelligence

Maaden Saudi Arabian Mining Company

Riyadh

On-site

SAR 300,000 - 540,000

Full time

13 days ago

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Job summary

Maaden Saudi Arabian Mining Company seeks a Senior Specialist for Data Science & AI II to lead and deploy advanced analytics, ML, and GenAI initiatives across the enterprise.

You will design scalable AI applications, optimize models, and ensure governance, security, and ethical AI practices while delivering measurable business value and digital transformation.

Qualifications

BachelorB4s degree in Data Science, AI, Computer Science, or a related quantitative discipline.
Relevant experience in data science and AI across business environments.

Responsibilities

Develop and deploy ML, DL, and GenAI solutions addressing strategic and operational challenges.
Design scalable AI applications to improve decision-making and productivity.
Accelerate AI adoption through advanced analytics and emerging AI technologies.
Improve model accuracy and reliability via feature engineering and evaluation.
Ensure governance, security, and ethical AI practices across initiatives.

Skills

Machine Learning

Deep Learning

Generative AI

LLMs

Prompt Engineering

RAG

Data Preparation

Model Evaluation

Analytics

Education

BachelorB4s degree in Data Science, AI, CS

Tools

Python

SQL

TensorFlow

PyTorch

Scikit-Learn

MLOps

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.

Model Performance & Optimization

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.

Generative AI & LLM Enablement

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.

Data Preparation & Engineering Enablement

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.

AI Operations & Lifecycle Management

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.

Governance, Risk & Responsible AI

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.

What Success Looks Like

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.

Minimum Qualifications

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

Experience

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.

Skills That Matter Functional Expertise