About The Position

TGS provides scientific data and intelligence to companies active in the energy sector. In addition to a global, extensive and diverse energy data library, TGS offers specialized services such as advanced processing and analytics alongside cloud-based data applications and solutions. At TGS, our Data Science team is leading the way in applying AI, machine learning, and cloud-scale computing to some of the toughest challenges in energy and geoscience. We work with one of the most extensive and well-organized energy data libraries in the world - AI-ready by design - giving our scientists and engineers a distinct edge in developing transformative solutions. From foundation models to multimodal AI systems, we are developing next-generation technologies that speed up interpretation, improve workflows, and provide actionable insights. Joining TGS means working directly with cutting-edge ML frameworks, large-scale cloud infrastructure, and advanced data pipelines, while collaborating with top experts across geoscience, engineering, and technology.

Requirements

  • Master’s or Ph.D. in Data Science, Computer Science, Engineering, or a related quantitative field.
  • 5–7 years of experience applying machine learning to real-world data problems, preferably in the energy domain.
  • Proven track record of delivering ML solutions from concept to production.
  • Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
  • Familiarity with cloud-based workflows (AWS preferred) and scalable ML pipelines.

Responsibilities

  • Develop and refine advanced ML models to optimize energy operations and improve exploration outcomes.
  • Lead technical project teams in designing, testing, and deploying robust data-driven solutions.
  • Mentor mid-level data scientists, sharing best practices in model development and handling energy datasets.
  • Drive continuous improvement in model performance, data quality, and workflow efficiency.
  • Collaborate with engineers, geoscientists, and software teams to integrate solutions into operational workflows.
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