Lead Data Scientist

TGS; Houston (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

TGS seeks a Lead Data Scientist to advance scientific machine learning for complex subsurface problems, combining hands‑on model development with technical leadership across major initiatives. You will help build reusable learning systems for subsurface data and guide cross‑team collaboration with senior data scientists and engineering partners.

The role emphasizes scientific rigor, reproducibility, and mentorship, contributing to the design and deployment of large‑scale learning systems within

Qualifications

• Requires MSc/PhD in a technical field.
• 5–10 years of applied data science or research‑oriented ML.
• Strong background in deep learning and scientific ML for large datasets.
• Experience leading technical initiatives or complex modeling projects.
• Energy, geoscience, or large‑scale industrial domains preferred.

Responsibilities

• Lead design, implementation, and evaluation of scientific ML models for subsurface and energy data.
• Guide development of large-scale representation learning systems.
• Provide technical reviews to ensure robustness, generalization, and reproducibility.
• Own major workstreams from research to deployment.
• Collaborate with senior data scientists, domain experts, and engineers.
• Mentor data scientists and share knowledge across the org.
• Engage in external research activities and publications.

Skills

Scientific ML

Leadership

Experimentation

Collaboration

Mentorship

Education

MSc/PhD in ML/Data Science

Tools

Deep Learning Frameworks

Representation Learning

Job description

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.

Purpose & Scope

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.

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.

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.

If you meet the qualifications and are passionate contributing to our team, we encourage you to submit your application by 08/15/2026.