Lead Data Scientist

TGS provides scientific data and intelligence to the global energy sector, enabling energy for all by unlocking vital, data-driven solutions and knowledge. Through an extensive and diverse energy data library, advanced analytics, cloud-based applications, and specialized services, we work in a way that is Passionate, Results-Driven, Collaborative, and Responsible.

About the Role

The Lead Data Scientist serves as a senior technical contributor within TGS’s Data Science organization, providing strong expertise in scientific machine learning and advanced analytics for complex subsurface problems. This role combines hands-on model development with technical leadership across major initiatives, supporting the development of reusable learning systems for subsurface data, including foundation-model–style representation learning. The position emphasizes scientific rigor, technical influence, and cross-team collaboration, contributing to the design and evolution of large-scale learning systems while working alongside other senior technical leaders.

Responsibilities

• Lead the design, implementation, and evaluation of scientific machine learning models for subsurface and energy-related data.
• Contribute to the development of large-scale representation learning systems, including self-supervised and weakly supervised approaches.
• Provide technical guidance and review for complex modeling initiatives, ensuring robustness, generalization, and reproducibility.
• Own major technical workstreams and deliver scalable analytical solutions from research through deployment.
• Collaborate closely with senior data scientists, domain experts, and engineering teams to align technical solutions with business and scientific objectives.
• Guide experimentation practices, model evaluation standards, and technical documentation.
• Mentor data scientists and support knowledge sharing across the organization.
• Participate in external research activities, publications, or technical collaborations.

Key Competencies

• Scientific Machine Learning Expertise: Strong understanding of ML applied to physical or scientific systems.
• Large-Scale Representation Learning: Experience with modern deep learning architectures and training workflows for complex datasets.
• Technical Leadership: Ability to guide technical workstreams and influence outcomes through expertise.
• Experimental Rigor: Strong focus on hypothesis-driven development and reproducible experimentation.
• Collaborative Influence: Works effectively within multi-lead, interdisciplinary environments.
• Mentorship: Supports development of technical talent and best practices.

Qualifications

• MSc or PhD in Machine Learning, Data Science, Applied Mathematics, Physics, Geophysics, or a related technical discipline.
• 5–10 years of experience in applied data science or research-oriented machine learning roles.
• Strong background in modern deep learning and scientific ML applied to complex or large-scale datasets.
• Experience leading technical initiatives or complex modeling projects.
• Experience in energy, geoscience, or large-scale scientific/industrial domains preferred.