10451 Clay Road
Houston, United States

Data Scientist III

Purpose & Scope

The Data Scientist III is a proficient individual contributor who independently designs, develops, implements, and evaluates machine-learning and advanced-analytics solutions for moderately complex business, scientific, and technical problems.

The role is expected to operate with substantial autonomy, take ownership of technical work from problem definition through deployment and evaluation, and translate data-science methods into reliable, usable solutions. The Data Scientist III contributes to technical direction within projects, collaborates across functions, and supports the development of less-experienced team members.

Within TGS Data Science, the role may support subsurface, seismic, well, Multi-Client, operational, or enterprise AI applications.

Key Responsibilities

Machine Learning & Advanced Analytics

• Design, develop, train, and evaluate machine-learning and deep-learning models for business and scientific applications.
• Apply appropriate statistical, machine-learning, deep-learning, and representation-learning techniques to moderately complex problems.
• Define experiments, evaluation metrics, baselines, and validation approaches.
• Analyze model performance and communicate results, limitations, and recommendations.
• Select appropriate methods and make technical decisions with limited supervision.

End-to-End Solution Development

• Take ownership of data-science solutions from problem definition through implementation, validation, deployment, and ongoing improvement.
• Develop production-oriented ML workflows rather than limiting work to exploratory modeling or prototypes.
• Integrate models into applications, platforms, or operational workflows.
• Contribute to backend services, APIs, data pipelines, cloud infrastructure, and user-facing components when required for successful delivery.
• Apply appropriate monitoring, testing, versioning, and reproducibility practices.

Data & Architecture

• Work with large, complex, and heterogeneous datasets.
• Develop and maintain data preparation, feature engineering, quality-control, and model-input pipelines.
• Provide input into data architecture and technical design decisions.
• Promote scalable and reusable approaches to data access, model training, inference, and deployment.
• Follow software-engineering and data-management best practices.

Technical Ownership & Delivery

• Independently plan and execute assigned technical work.
• Own significant components or workstreams within larger programs.
• Identify technical risks and recommend appropriate solutions.
• Balance scientific rigor, delivery requirements, computational constraints, and business objectives.
• Drive work through completion with limited day-to-day supervision.
• Support transition of research and prototypes into operational capabilities.

Collaboration & Business Impact

• Work effectively with data scientists, software engineers, geoscientists, domain experts, product owners, and business stakeholders.
• Translate business or scientific requirements into technical solutions.
• Understand the business context and intended value of the work being performed.
• Communicate technical concepts and results clearly to both technical and non-technical stakeholders.
• Incorporate stakeholder and user feedback into solution development.
• Contribute to projects that have measurable operational, commercial, or strategic impact.

Technical Leadership & Knowledge Sharing

• Provide technical guidance within assigned projects and workstreams.
• Review technical approaches and contribute to design discussions.
• Share expertise and best practices with other team members.
• Mentor junior data scientists, interns, or colleagues when appropriate.
• Contribute to internal technical presentations, documentation, publications, patents, or external technical visibility where relevant.

Key Competencies

Machine Learning & Data Science

Strong working knowledge of:

• Machine learning and deep learning
• Model design, training, validation, and evaluation
• Experimental design and performance metrics
• Data preparation and feature engineering
• Modern ML frameworks and development environments
• Reproducible model-development workflows

Experience in areas such as representation learning, foundation models, generative AI, scientific machine learning, or domain-specific AI is valuable depending on assignment.

Education & Experience

• Bachelor's, Master's, or PhD degree in Data Science, Computer Science, Engineering, Mathematics, Physics, Geophysics, or a related quantitative discipline.
• Typically approximately 2–5 years of relevant professional experience, depending on education and demonstrated capability.
• Demonstrated experience developing machine-learning or advanced-analytics solutions.
• Experience working with substantial datasets and modern ML frameworks.
• Evidence of independently delivering technically meaningful work.