Company: DRL LLC

• Deliver petroleum engineering research objectives aligned with the team’s subsurface, production, and field-development roadmap, ensuring execution against defined milestones and timelines.
• Analyze, QC well performance data, gauge data analysis, understanding and interpretation of well performance based on gauge and other data.
• Support data validation, quality control, and structured data entry for petroleum engineering datasets, including well, production, and operational data.
• Assist senior engineers and researchers with technical studies, and industry-facing articles related to reservoir performance, drilling, completion, and production optimization.
• Apply both qualitative engineering judgment and quantitative analysis (statistics, trend analysis, correlations) in subsurface and production studies.
• Design data-collection templates, workflows, and procedures for petroleum engineering research projects, including field data capture and post-processing.
• Communicate and present engineering findings, interpretations, and recommendations to management, partners, and clients in a clear and structured manner.
• Collaborate closely with product design, software engineering, data science, and client teams to translate petroleum engineering workflows into digital and analytical solutions.
• Contribute to the design, implementation, and evaluation of petroleum engineering studies, including field pilots, algorithm validation, and performance assessment.

• BSc or MSc in Petroleum Engineering or a related field.
• Comfortable with coding and scripting for engineering and data analysis tasks (e.g., using Python or MATLAB)
• Familiarity with data science tools and libraries (e.g., Pandas, NumPy, Matplotlib, Excel)
• 1+ years' experience in relevant field
• Strong understanding of reservoir and production engineering fundamentals
• Ability to independently read and summarize technical papers and industry research
• Ability to work with field data: clean, analyze, and interpret results to support engineering decisions
• Good knowledge of research methodologies
• Good statistical and mathematical aptitude