Job Description & How to Apply Below
About us
At Exxon Mobil, our vision is to lead in energy innovations that advance modern living while reducing emissions. 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 Exxon Mobil to help create sustainable solutions that improve quality of life and meet society's evolving needs. Learn more about and how we can .
Job Group Capability Data Science, Digital & Analytics Job Group Computational & Data Sciences What Will You Do As a Senior AI Scientist, you will provide technical leadership in the design, development, and deployment of transformational AI solutions that solve some of the most complex challenges across Exxon Mobil's global business. You will operate at the intersection of advanced AI research, enterprise-scale implementation, and strategic business impact.
You will collaborate with data scientists, machine learning engineers, software developers, computational experts, and business leaders across the organization to shape AI roadmaps, architect scalable solutions, and deliver measurable value through advanced analytics and AI technologies.
Key responsibilities include:
Lead the end-to-end delivery of enterprise AI/ML solutions, from opportunity identification and technical strategy through deployment, productionization, monitoring, and continuous improvement.
Drive the design and implementation of advanced AI systems including Generative AI, agentic AI workflows, NLP, time-series forecasting, computer vision, optimization, and commercial analytics solutions.
Architect scalable and production-ready AI platforms leveraging MLOps best practices including CI/CD, model governance, monitoring, observability, MLflow, and automated retraining pipelines.
Apply advanced data science, machine learning, statistical analysis, and domain expertise to solve highly complex business and engineering challenges.
Translate complex business and engineering problems into mathematical, statistical, and AI-driven solutions with measurable operational or commercial impact.
Provide deep technical leadership across model architecture, feature engineering, experimentation, evaluation methodologies, and AI system design.
Partner with business stakeholders and senior leadership to define AI strategy, prioritize use cases, and align AI investments with enterprise objectives.
Mentor and guide data scientists and AI practitioners by promoting best practices in applied AI, experimentation, software engineering, and responsible AI.
Stay abreast of emerging AI technologies, research advancements, and industry trends, proactively evaluating and applying next-generation AI capabilities to drive innovation and strategic business value.
Evaluate emerging AI technologies, frameworks, and research trends to identify opportunities for innovation and competitive advantage.
Drive adoption of modern AI engineering principles including scalable inference, distributed training, LLMOps, workflow orchestration, and cloud-native AI architectures.
Ensure solutions meet enterprise standards for scalability, security, reliability, governance, and responsible AI practices.
About You
Skills and Qualifications
Master's or Ph.D. degree from a recognized university in Data Science, Computer Science, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, Geoscience/Geophysics, or related disciplines with a minimum GPA of 7.0.
8+ years of industry experience developing, deploying, and scaling enterprise-grade AI/ML solutions in production environments.
Demonstrated expertise in one or more of the following areas:
Generative AI and Large Language Models (LLMs)
Agentic AI systems and orchestration frameworks
Natural Language Processing
Time-Series Forecasting
Computer Vision
Reinforcement Learning
Commercial Analytics and Optimization
Strong expertise in statistical learning, machine learning, deep learning, Bayesian methods, causal inference, and advanced optimization techniques.
Proven experience leading end-to-end AI solution delivery from business problem formulation to deployment and operationalization at enterprise scale.
Deep practical experience with modern AI/ML frameworks and ecosystems including PyTorch, Tensor Flow, scikit-learn, Hugging Face, Lang Chain, MLflow, and distributed compute environments.
Strong programming expertise in Python and experience with modern software engineering practices, APIs, testing frameworks, Git, and Agile development methodologies.
Experience deploying AI solutions on cloud and enterprise platforms such as Azure, Databricks, Kubernetes, or equivalent ecosystems.
Strong…
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