Machine Learning and AI Apprentice

Position Summary

SLB is seeking a passionate and skilled Machine Learning and AI Apprentice to join our dynamic Reservoir Performance BI team. This apprenticeship provides an opportunity to work on cutting-edge projects and gain hands-on experience in machine learning, generative AI, data science, and artificial intelligence. The apprentice will collaborate with our expert team of data scientists and AI engineers, contributing to innovative solutions that drive business growth and technological advancement.

Key Responsibilities

• Assist in developing, training, and evaluating machine learning models and algorithms.
• Collaborate on projects involving generative AI techniques such as GANs and variational autoencoders.
• Analyze large datasets to extract meaningful insights and patterns.
• Implement data preprocessing and feature engineering strategies.
• Contribute to the development and deployment of AI-based applications and tools.
• Conduct research on the latest trends and advancements in AI and machine learning.
• Document and present findings, methodologies, and results to the team.

Requirements

• Currently enrolled in a Master’s degree program or PHD in Computer Science, Data Science, Artificial Intelligence, or a related field.
• Strong understanding of machine learning algorithms and principles.
• Experience with generative AI techniques and frameworks (e.g., TensorFlow, PyTorch).
• Proficiency in programming languages such as Python, R, or Julia.
• Familiarity with data preprocessing, feature engineering, and data visualization.
• Excellent analytical and problem-solving skills.
• Strong communication skills and the ability to work in a team environment.
• Enthusiasm for learning and staying updated with the latest trends in AI and data science.
• Proficiency in English, both written and spoken.
• Strong interpersonal skills and the ability to effectively communicate complex technical concepts to non-technical stakeholders.

Preferred Qualifications

• Previous internship or project experience in machine learning, AI, or data science.
• Knowledge of deep learning architectures and techniques.
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying AI models.
• Familiarity with natural language processing (NLP) and computer vision techniques.