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Data Scientist, Wells Engineering

Engineering

Houston, TX

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

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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Build the Software that Powers Well Assurance.