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Senior Specialist, Data Science & Artificial Intelligence

Ma'aden

πŸ“ Riyadh, Saudi Arabia
πŸ’Ό Full-time
πŸ•’ Posted 2 months ago

Job description

Role Overview

Senior Specialist, Data Science & Artificial Intelligence at Ma'aden. This role designs, develops, and deploys machine learning and Generative AI solutions to solve defined business problems, ensuring technical robustness, model performance, and responsible AI implementation.

Role Purpose

Design, develop, and deploy machine learning and Generative AI solutions to solve defined business problems, ensuring technical robustness, model performance, and responsible AI implementation.

Key Responsibilities

Model Development & Deployment

  • Develop, test, and validate machine learning, deep learning, and Generative AI models, including LLM-based applications.
  • Implement prompt engineering strategies and retrieval-augmented generation (RAG) pipelines.
  • Support deployment of ML and GenAI models using MLOps and LLMOps practices.
  • Support monitoring, retraining, and lifecycle management of deployed models.

Data Preparation & Optimization

  • Prepare, cleanse, and transform structured and unstructured datasets.
  • Perform feature engineering, model tuning, and performance optimization.

Model Evaluation & Quality Assurance

  • Conduct structured evaluation of LLM outputs, including hallucination detection and quality scoring.
  • Document model design, assumptions, risks, and validation results.

Integration & Governance

  • Integrate AI solutions via APIs into enterprise systems.
  • Ensure compliance with data governance, cybersecurity, and ethical AI guidelines.

Qualifications & Experience

  • Bachelor's degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative field.
  • 4–6 years in data science, machine learning, or applied AI roles.

Skills & Competencies

  • Python
  • SQL
  • ML frameworks: TensorFlow, PyTorch, Scikit-learn
  • Prompt engineering
  • RAG pipelines and vector databases
  • Model fine-tuning and evaluation
  • Data preprocessing and feature engineering
  • Basic cloud deployment concepts
  • MLOps / LLMOps fundamentals
  • Analytical problem-solving
  • Results orientation
  • Collaboration
  • Continuous improvement
  • Accountability

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