Data Scientist

At IPT Global, this Data Scientist role supports safer, faster, and more cost-efficient field operations by building end-to-end machine learning models from well barrier assurance and verification data. You will blend wells engineering and rig-to-model operational intuition with practical data science, helping teams turn complex technical signals into predictions and clear, actionable recommendations.

Responsibilities

• Build and apply models to support pressure test interpretation, including positive and inflow tests.
• Evaluate time series data from well control equipment, such as BOPs and associated control systems, and develop predictive maintenance models.
• Develop monitoring models for sustained or abnormal annulus pressure behavior across the well lifecycle.
• Track barrier element status and verification history to identify gaps and help prioritize integrity work.
• Clean, structure, and prepare data so models can be trusted.
• Communicate model outcomes clearly to wells engineers, integrity teams, and leadership, including when not to rely on the model.

Requirements

• A degree in petroleum, mechanical, or a related engineering discipline.
• Demonstrable, hands-on wells engineering or well integrity experience at an operator, drilling contractor, or oilfield services company.
• Working proficiency with a range of data science tools.
• At least one model or analytical tool you built that was actually used to inform engineering or operational decisions.
• A solid grasp of statistics, plus the judgment to know when an engineering assessment is more appropriate than a data-driven approach.

Technologies

Python, SQL

Nice to have

• Experience with well integrity management systems or barrier assurance workflows.
• Experience with time series analysis or anomaly detection on pressure and sensor data.
• Exposure to cloud platforms or deploying models into production.
• Additional SQL experience.

Location: Houston, TX (onsite)

All information will be kept confidential according to EEO guidelines.