10451 Clay Road
Houston, United States

The Data Science (R&T) Intern will work closely with the Data Science team to address real-world challenges in subsurface and geoscience data. The intern will apply modern machine learning and data science techniques to seismic, well, and related datasets to improve exploration workflows, decision-making, and automation within the energy sector. The role combines hands-on model development with data engineering, experimentation, and clear communication of results to technical and non-technical stakeholders. 

Responsiblities:

Machine Learning & Data Science

• Develop, implement, and evaluate machine learning models for seismic, well, and production datasets.

Data Processing & Validation

• Perform data cleaning, preprocessing, and quality control on large, heterogeneous datasets.
• Build and maintain reproducible data pipelines and scripts for feature engineering and labeling.

Research & Innovation

• Conduct literature reviews on state-of-the-art methods in machine learning, computer vision, and scientific AI relevant to geoscience.
• Prototype new ideas, compare against baselines, and help document findings in short technical notes or internal reports.

Visualization & Communication

• Create clear, compelling visualizations (figures, dashboards, and plots) to communicate data insights and model performance.
• Prepare concise slide decks and contribute to internal presentations, workshops, or marketing materials showcasing project outcomes.

Collaboration & Engineering Practices

• Work in close collaboration with data scientists, geoscientists, and software engineers to understand problem requirements and constraints.
• Use standard engineering practices (version control, code reviews, documentation) to ensure code quality and reproducibility.

Key Competencies

1. Strong Analytical Thinking – Able to frame problems, explore hypotheses, and interpret model results with a critical mindset.
2. Technical Proficiency – Comfortable working in Python with common data science and ML libraries (e.g., NumPy, pandas, PyTorch and/or TensorFlow, scikit-learn, Matplotlib/Plotly).
3. Data Handling at Scale – Experience (coursework or projects) dealing with large or complex datasets, including data cleaning, feature engineering, and pipeline design.
4. Communication & Storytelling – Able to explain technical concepts clearly to both technical and non-technical audiences, in writing and in presentations.
5. Collaboration & Initiative – Works well in a team, asks thoughtful questions, and shows curiosity and initiative in exploring new methods or tools.
6. Learning Mindset – Demonstrated interest in staying current with developments in AI/ML and applying them to real-world problems.

Qualifications:

Required:

1. Currently enrolled in a Bachelor’s, Master’s, or PhD program in Data Science, Computer Science, Electrical Engineering, Applied Mathematics, Geophysics, or a related field.
2. Solid programming skills in Python and familiarity with core data science tools (e.g., Jupyter, Git, NumPy/pandas, scikit-learn).
3. Coursework or project experience in machine learning (supervised and/or unsupervised) and basic statistics.
4. Ability to work independently on well-defined tasks and to manage time across multiple priorities.
5. Strong written and verbal communication skills in English.

Preferred:

1. Experience with deep learning frameworks (PyTorch and/or TensorFlow) and GPU-accelerated training.
2. Exposure to geoscience, subsurface data, or scientific computing (e.g., seismic data, well logs, numerical simulation, signal processing).
3. Familiarity with cloud computing environments (AWS, Azure, or GCP) and containerization tools (Docker) is a plus.
4. Prior internship or research experience in applied machine learning or data science.