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Strong Tier-1 brand, metro location, common data-scientist role and mid-level experience increase candidate competition.
Core ML skills are transferable, but oil-and-gas domain preference raises domain-specific fit requirements.
Mandatory advanced degree, 5+ years and production MLOps experience enforce strict technical hiring filters.
Lead end-to-end delivery of AI/ML solutions including scoping, modeling, evaluation, deployment, and monitoring within the oil and gas industry.
Develop and deploy production-ready data science tools, models, or software including GenAI/NLP, time-series, computer vision, and commercial analytics applications.
Apply advanced data science methodologies and MLOps best practices to generate actionable insights and optimized recommendations.
Master’s or Ph.D. degree in Data Science, Computer Science, IT, Chemical Engineering, Mechanical, Civil, Materials, Aerospace, Geoscience/Geophysics, Applied Math or related discipline with minimum GPA of 7.0.
Minimum 5+ years relevant experience in developing and deploying production-ready AI/ML solutions.
Proficiency with Python/R programming, ML frameworks (PyTorch, TensorFlow, scikit-learn), and MLOps tools (MLflow, CI/CD).
Experience with full machine learning lifecycle and software engineering practices including agile methodologies and version control (Git).
Experienced in applying advanced machine learning methods including deep learning, Bayesian techniques, causal inference, and statistical analysis in enterprise settings.
Demonstrates ability to deliver AI/ML solutions across teams globally, integrating domain knowledge from oil and gas or commercial sectors (preferred but not mandatory).
Familiar with cloud data science platforms such as Azure Databricks and with skills in mathematical modeling, physics-based simulation as added advantage.