At ExxonMobil, our vision is to lead in energy innovations that advance modern living and a net-zero future. As one of the world's largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for.
The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies.
We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society's evolving needs. 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.
Time Series Analysis, Computer Vision, Natural Language Processing, Generative AI, Commercial Analytics.
Data Science, Computer Science, IT, Chemical Engineering, Mechanical, Civil, Materials, Aerospace, Geoscience/Geophysics, Applied Math or related disciplines with a minimum GPA of 7.0.
5+ years of relevant experience in developing, delivering, and validating production-ready AI/ML solutions.
In-depth knowledge and practical experience in statistical analysis techniques (e.g., classification, regression, time-series, Bayesian techniques) and machine learning techniques (e.g., decision trees, ensemble methods, deep learning, neural networks, causal analysis).
Practical experience in the full machine learning lifecycle from problem formulation, data acquisition, data cleaning to model building and deployment at enterprise https://jobeax.com/link/AsTDTp6i8kJTEE83 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).
Applied Data Science
Statistical Modeling & Analysis
Machine Learning & Deep Learning
Generative AI, NLP, Computer Vision
Time Series Analysis & Forecasting
End‑to‑End ML Project Lifecycle
Python/R Programming Skills
Software Engineering & Agile Framework
Preferred Experience

Prior experience with oil & gas, commercial domain, supply chain, production systems, wells or subsurface domain is highly desirable.
Experience working with Azure Databricks or other data science frameworks.
Experience with mathematical modeling, physics-based simulators, scientific computing and numerical methods would be an added advantage .
Machine Learning
Applied Software Engineering for Data
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Sustainability, Software Engineer, Chemical Engineer, Computer Science, Energy, Engineering, Research, Technology

Data Scientist Ml Ai Python

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