Lead Data Scientist

About the role

Lead Data Scientist

Position Type: Permanent, Full-Time

Department: Data Science

Direct Supervisor: Director, Data Science

Job Summary

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.

The 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.

Key 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.

Qualifications and Requirements

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 machine learning applied to complex or large-scale datasets.

Experience leading technical initiatives or complex modeling projects.

Experience in energy, geoscience, or large-scale scientific or industrial domains is preferred.

Key Competencies

Scientific Machine Learning Expertise: Strong understanding of machine learning 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 the development of technical talent and best practices.