AI/ML Solutions Lead

Job title: AI/ML Solutions Lead

About the Role ExxonMobil is seeking a results-driven data scientist/AI engineer to lead data science solutions across the oil and gas lifecycle. The role involves collaborating with global teams to research, develop, and deliver AI/ML tools and software that solve complex business problems, from exploration to operations, with a focus on production-ready, scalable solutions.

What You'll Do

• Work with data scientists, data analysts, computational engineers, machine learning engineers, software developers, or business representatives across our global organization to research, develop, and deliver data science tools, models, or software for solving challenging business problems in the oil and gas industry.
• Lead end-to-end delivery of AI/ML solutions: scoping, modeling, evaluation, deployment, and monitoring.
• Develop GenAI/NLP applications, and/or time-series, computer vision, commercial analytics models.
• Build production-ready solutions applying MLOps best practices (MLflow, CI/CD, monitoring, data quality).
• Apply data science methods, machine learning tools, visualization and/or statistical techniques along with domain knowledge to generate actionable insights and provide optimized recommendations.

What We're Looking For

• Expertise in Time Series Analysis, Computer Vision, Natural Language Processing, Generative AI, Commercial Analytics.
• Master’s or Ph.D. in Data Science, Computer Science, IT, Chemical Engineering, Mechanical, Civil, Materials, Aerospace, Geoscience/Geophysics, Applied Math or related with minimum GPA 7.0.
• 5+ years of relevant experience developing, delivering, and validating production-ready AI/ML solutions.
• In-depth knowledge of statistical analysis techniques (e.g., classification, regression, time-series, Bayesian techniques) and ML techniques (e.g., decision trees, ensemble methods, deep learning, neural networks, causal analysis).
• Full ML lifecycle experience from problem formulation, data acquisition, data cleaning to deployment at enterprise level.
• Proficiency in Python or R, ML frameworks (PyTorch, TensorFlow, scikit-learn) and libraries (NumPy, pandas).
• Experience with software engineering practices, agile methodologies and version control (Git).
• Strong communication and interpersonal skills; ability to work collaboratively in a global team.

Nice to Have

• Excellent problem-solving and attention to detail.
• Prior oil & gas, commercial domain, supply chain, production systems, wells or subsurface domain desirable.
• Experience with Azure Databricks or other data science frameworks.
• Experience with mathematical modeling, physics-based simulators, scientific computing and numerical methods.