Expert Data Scientist

About us At ExxonMobil, 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 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 . 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 ExxonMobil’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. 12 + 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, TensorFlow, scikit-learn, Hugging Face, LangChain, 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 communication, stakeholder management, and technical leadership skills with the ability to influence across technical and business organizations. Demonstrated passion for continuous learning and staying current with rapidly evolving AI technologies, tools, frameworks, and research trends. Demonstrated ability to lead ambiguity, drive innovation, and deliver high-impact AI initiatives in complex enterprise environments. Preferred Experience Experience applying AI solutions within oil & gas, energy, manufacturing, commercial optimization, supply chain, production systems, wells, or subsurface domains. Experience with scientific computing, optimization algorithms, numerical methods, physics-based modeling, and digital twin technologies. Familiarity with modern GenAI ecosystems including vector databases, retrieval-augmented generation (RAG), AI agents, and foundation model fine-tuning. Experience leading cross-functional AI initiatives across geographically distributed teams. Track record of mentoring technical teams and driving enterprise AI adoption. Publications, patents, open-source contributions, or applied research experience in AI/ML-related domains are considered an 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.