GenAI Data Scientist, Commercial, Supply Chain & Trading

ExxonMobil•Spring, TX

43d

About The Position

This role involves working on complex AI use cases from ideation and discovery through deployment and sustainment as part of integrated, enterprise-level teams. The Data Scientist will collaborate with ExxonMobil subject matter experts to strengthen organizational capabilities in AI/ML and support business teams in making impactful, data-driven decisions.

Requirements

5+ years of professional experience in developing and deploying AI/ML solutions, with a strong emphasis on Generative AI technologies and a solid background in applying these methods to physical systems and engineering workflows.
Deep expertise in natural language processing (NLP), large language models (LLMs), and building agentic workflows for Generative AI with the ability to understand underlying mathematics and develop novel algorithms.
Solid understanding of knowledge graphs, ontologies, and semantic technologies.
Solid foundation and experiences in AI/ML on data processing, probability and statistics, EDA, feature engineering, modeling strategy, model development, and explainable AI.
Proven track record of successfully developing and deploying multiple end-to-end Generative AI solutions, notably multi-agent systems, in business environments.
Proficient in Python and widely used ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Proficient in Generative AI frameworks such as Langchain, Promptflow, or Copilot Studio.
Proven ability to uncover meaningful insights from complex datasets.
Strong drive toward hands-on testing of hypotheses and rapid validation of assumptions through data.
Strong communication, collaboration, and problem-solving skills.
Advanced degree (Master’s or PhD) in Data Science, Computer Science, Engineering, or a related field.

Nice To Haves

Experience in the energy industry, preferably with a focus on subsurface or well-related topics.
Experience in implementing hybrid search systems in conjunction with Large Language Models, e.g. Retrieval Augmented Generation (RAG) solutions.
Proficient in using the Databricks platform, Azure Open AI, Azure managed NLP services and OCR technologies.
Demonstrated ability to thrive in a collaborative Agile product team setting.

Responsibilities

Lead the scoping, design, development, and deployment of AI/ML solutions, primarily focused on Generative AI applications for subsurface and well-related operations.
Collaborate with data and machine learning engineers to operationalize models and ensure seamless integration with existing digital infrastructure.
Build and deploy AI system components (such as chatbots, agents, and other workflow automation components) capable of interacting with diverse data sources and providing insightful information to users.
Develop and implement solutions involving orchestration of multiple AI agents to achieve complex tasks.
Apply domain knowledge and physical principles to improve model accuracy and reliability.
Contribute to the growth of internal AI capabilities by sharing expertise and developing best practices.
Provide technical mentorship and guidance to colleagues across teams.
Work closely with team leads and subject matter experts to align on project priorities, strategy, and solution design.
Optimize end to end AI solutions to enhance performance, usability, and cost.
Work closely with business to understand business problem and translate that into mathematical framework. Also, work with business to help enhance business adaptability for the solution.
Validate AI system responses, including troubleshooting prompt engineering, agentic workflows and other aspects of a GenAI system.
Develop process and automation for accelerating validation of AI systems, while ensuring the proper degree of accuracy is maintained.