Qualifications

• Degree in data science, computer science, statistics, engineering, physics, or a related quantitative field.
• Strong proficiency in Python and its data science libraries (pandas, NumPy, scikit-learn) with experience in time series modeling, anomaly detection, or predictive maintenance.
• Experience building a model or analytical tool that informed engineering or operational decisions.
• Solid understanding of statistics and model validation, with the ability to discern when engineering assessments are preferable to data-driven ones.
• Experience with physical or sensor-derived data, preferably in oil and gas or related industrial sectors.

Responsibilities

• Build models to support pressure test interpretation, including positive and inflow tests.
• Evaluate time series data from well control equipment and develop predictive maintenance models.
• Develop monitoring and anomaly detection models for abnormal annulus pressure behavior.
• Track barrier element status and verification history to identify gaps and prioritize integrity work.
• Clean and structure data for reliable model input.
• Clearly explain model results to wells engineers, integrity teams, and leadership, including limitations of the models.

Benefits

• Opportunity to see the direct impact of your work on operational decisions.
• Work in a collaborative environment with a focused team of around 100 people.
• Engage in meaningful projects that go beyond pilot phases.