Advanced Data Scientist

About us 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. Learn more about our What and our Why and how we can work together . What Will You 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. About You - Skills and Qualifications Expertise in one or more of the following: Time Series Analysis, Computer Vision, Natural Language Processing, Generative AI, Commercial Analytics. Master’s or Ph.D. degree from a recognized university in one of the following disciplines: 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 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, with the ability to work collaboratively in a global team environment. Key Skills 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 Excellent problem-solving skill and attention to detail. 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 . Functional Skills Deep & Reinforcement Learning Machine Learning Bayesian & Causal Inference Applied Software Engineering for Data Mathematical Framing of Business Problems Nothing herein is intended to override the corporate separateness of local entities. Working relationships discussed herein do not necessarily represent a reporting connection, but may reflect a functional guidance, stewardship, or service relationship. Exxon Mobil Corporation has numerous affiliates, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity, those terms and terms like corporation, company, our, we and its are sometimes used as abbreviated references to specific affiliates or affiliate groups. Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Similarly, ExxonMobil has business relationships with thousands of customers, suppliers, governments, and others. For convenience and simplicity, words like venture, joint venture, partnership, co-venturer, and partner are used to indicate business relationships involving common activities and interests, and those words may not indicate precise legal relationships.