Data Scientist, Upstream Operations

Lead the design, development, and deployment of advanced AI/ML solutions for upstream oil and gas operations. Build predictive and optimization models from production, well, and sensor data, and help integrate upstream digital analytics capabilities into the Modeling, Optimization, and Data Science (MODS) organization.

• Lead end-to-end design/development/deployment of AI/ML solutions for upstream operations
• Analyze production, well, sensor/IoT, and log data for forecasting and value creation
• Build GenAI and predictive/optimization/time-series models tailored to upstream workflows
• Ensure data quality, explainability, and governance for model reliability
• Use MLOps (CI/CD, MLflow, monitoring, cost optimization) and migrate/enhance upstream digital analytics into MODS

Focus on production-grade AI/ML and GenAI applications for upstream domains such as production optimization, predictive maintenance, and anomaly detection. The stack includes Python with PyTorch, TensorFlow, and scikit-learn, plus Databricks/Spark, and production deployment using MLOps with CI/CD and MLflow monitoring and cost optimization.

The ideal candidate has 5+ years delivering production AI/ML solutions, ideally in upstream oil and gas or heavy industrial operations. They are strong in Python and machine learning (PyTorch, TensorFlow, scikit-learn) and have hands-on MLOps experience with CI/CD and MLflow, including monitoring and cost optimization. They can translate upstream operational data (production, well performance, sensor/IoT, logs) into reliable predictive and optimization models, including GenAI applications when needed.