Data Science Geologist

Requirements

• Background in Petroleum Engineering

Additional Information

CURRENT EMPLOYEES - Please apply using "Jobs Hub" in Workday. This career site is for external applicants only. This Data Science Geologist will bridge traditional earth sciences with advanced data science and analytics to drive more informed subsurface decisions. This role focuses on automating geological workflows, integrating complex subsurface datasets, and developing predictive and geospatial models to improve reservoir characterization, resource discovery, and development outcomes. The position plays a critical role in translating advanced analytical insights into actionable guidance for both technical teams and business leadership. Job Responsibilities: Include but are not limited to Predictive Modeling: Develop and apply machine learning and geospatial models to identify geological features and predict reservoir quality. Data Integration: Clean, integrate, and analyze diverse datasets, including seismic, well log, geochemical, rock-based, and petrophysical data. Spatial Analysis: Leverage GIS and subsurface mapping tools to perform complex spatial analysis and create interactive geological models and maps. Automated Workflows: Design, build, and maintain data pipelines to automate routine geological analysis and reporting. Stakeholder Communication: Translate complex technical analyses into clear, actionable insights for technical teams and business stakeholders. Required Qualifications: Bachelor's degree in Geoscience or a related field Minimum of 5 years of data geoscience experience Proficiency in Python (Pandas, Scikit-learn, PyTorch ) or R for statistical analysis Expertise in SQL and industry-standard geological databases Experience with GIS tools such as ArcGIS or QGIS, and/or 3D subsurface modeling software (e.g., Petrel) Strong understanding of machine learning techniques (clustering, regression, anomaly detection) applied to subsurface data Ability to visualize and communicate spatial data using tools such as Power BI, Tableau, or Python libraries (Matplotlib, Seaborn)

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