Advanced Data Scientist

Overview

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 Role You Will Play In Our Team
As a Data Scientist, you will drive end to end data science and AI solutions to solve complex oil and gas challenges. You will apply advanced machine learning, statistical modeling, and programming across diverse domains including Generative AI & NLP, time‑series forecasting, commercial analytics and computer vision. The role involves working closely with engineers and business stakeholders, and delivery of scalable and production ready solutions starting from problem scoping and experimentation through deployment and sustainment.

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.
  
  

• 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
  
  

• 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.
  
  

Alternate Location:

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