Senior Engineer, Data Science

Duties And Responsibilities

• Leads the design, development, and deployment of Artificial Intelligence/Machine Learning solutions for upstream subsurface and well operations, including physics-informed and hybrid modeling approaches for reservoir, drilling, and production optimization.
• Builds advanced Artificial Intelligence/Machine Learning solutions for commercial analytics use cases such as pricing, supply chain, marketing, and trading to improve profitability and decision speed.
• Executes complex AI initiatives from ideation and discovery through model development, deployment, and sustainment as part of integrated, enterprise-level teams.
• Architects and implements reliable data pipelines and features using modern data platforms (e.g., Databricks, cloud services), ensuring data quality, lineage, and performance for analytics workloads.
• Applies Machine Learning Ops best practices to automate training, testing, deployment, monitoring, and model lifecycle management at scale in production environments.
• Translates complex business problems into analytical approaches with clear hypotheses, success criteria, and measurable outcomes across upstream and commercial domains.
• Develops and delivers communications that convey a clear understanding of technical concepts, model results, and business implications to diverse technical and non-technical audiences.
• Buils strong partnerships and cross-functional relationships with geoscience, engineering, operations, commercial, IT, and leadership stakeholders to drive adoption and sustain business impact.
• Gains the confidence and trust of others through honesty, integrity, and follow-through while championing responsible and secure use of data and AI.
• Actively seeks new ways to grow and be challenged by staying current on emerging Artificial Intelligence/Machine Learning, generative AI, optimization, and computational techniques relevant to energy and integrating them where they add value.

Skills And Competencies

• Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.
• Action oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
• Drives results - Consistently achieving results, even under tough circumstances.
• Self-development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
• Nimble learning - Actively learning through experimentation when tackling new problems, using both successes and failures as learning fodder.
• Situational adaptability - Adapting approach and demeanor in real time to match the shifting demands of different situations.
• Instills trust - Gaining the confidence and trust of others through honesty, integrity, and authenticity.

Required Qualifications

• Bachelor of Science in Petroleum, Mechanical, Chemical, or related Engineering discipline from an accredited college or university and Master of Science in Data Science, or a closely related data science or analytics field, from an accredited college or university.
• Minimum five (5) years of hands-on experience delivering production-grade data science/Machine Learning solutions, including end-to-end lifecycle from discovery to deployment and sustainment.
• Proficiency in Python and SQL; experience with Machine Learning frameworks and tooling (e.g., scikit-learn, PyTorch/TensorFlow), and data platforms such as Databricks and cloud services.
• Experience building and maintaining data pipelines and features and applying Machine Learning Ops practices for model deployment and monitoring in enterprise environments.
• Demonstrated ability to partner with technical and business domains in energy, including upstream subsurface, drilling/completions, production operations, and/or commercial analytics such as pricing, supply chain, marketing, or trading.
• An acceptable pre-employment background and drug test.

Preferred Qualifications

• Oil and gas industry experience, particularly in upstream engineering, subsurface, drilling and completions, production operations, or commercial energy analytics.
• Background in computational sciences, optimization, or high-performance computing for engineering applications.
• Familiarity with enterprise data governance, security, and responsible AI practices in regulated environments.
• Five (5) or more years of combined oil and gas engineering/domain experience and applied data science experience.