Data Scientist, Wells Engineering

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Description

What you'll work on Building models to support pressure test interpretation, including positive and inflow tests Evaluating time series data from well control equipment, such as BOPs and associated control systems, and developing predictive maintenance models Developing monitoring and anomaly detection models for sustained or abnormal annulus pressure behavior across the well lifecycle Tracking barrier element status and verification history to surface gaps and prioritize integrity work Cleaning and structuring data into something models can trust Explaining model results clearly to wells engineers, integrity teams, and leadership, including when the model shouldn't be trusted Требования: What you bring A degree in data science, computer science, statistics, engineering, physics, or a related quantitative field Strong proficiency in Python and its data science ecosystem (pandas, NumPy, scikit-learn, and similar), with experience in at least one of time series modeling, anomaly detection, or predictive maintenance At least one model or analytical tool you built that was actually used to inform engineering or operational decisions A solid grasp of statistics and model validation, and the judgment to know when an engineering assessment beats a data-driven one Experience working with physical or sensor-derived data, ideally in oil and gas, wells engineering, or well integrity, or in another industrial setting such as manufacturing, energy, or aerospace Nice to have Hands-on wells engineering or well integrity experience at an operator, drilling contractor, or oilfield services company Familiarity with well integrity management systems or barrier assurance workflows Exposure to cloud platforms or deploying models into production SQL experience

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