Reservoir Engineer; Data Science

Description

Fervo is working to build the most cost-effective, repeatable geothermal power plants in the world. Delivering on this mission requires operational excellence across every function — including the production engineering systems, workflows, and technical standards that ensure our geothermal assets operate safely, reliably, and efficiently as we scale.

Joining Fervo as a Reservoir Engineer with a data science focus means owning subsurface evaluations and advancing analytics through reservoir modeling, data science, and AI. This role is ideal for an early-career engineer with experience or strong interest in geothermal and/or unconventional oil & gas systems who is motivated to solve complex subsurface problems.

Operating at the intersection of subsurface engineering and advanced analytics, this position drives the development of tools and models that improve operational efficiency, deepen subsurface understanding, and enhance analytical capabilities.

The position works closely with reservoir, geoscience, and data teams to build models, analyze large datasets, to apply modern computational techniques to real-world subsurface challenges.

• Build, update, and maintain reservoir simulation and analytical models to support forecasting, development planning, and optimization.
• Apply data science and machine learning techniques to reservoir characterization, production forecasting, and anomaly detection.
• Support history matching, sensitivity analyses, and scenario evaluations.
• Develop and maintain Python-based workflows, scripts, and tools to automate subsurface analyses and improve data quality.
• Integrate geological, petrophysical, stimulation, and operational data into reservoir studies in collaboration with cross-functional teams.
• Clearly communicate technical results through visualizations, presentations, and written reports.
• Stay current with emerging tools and best practices in reservoir engineering, analytics, and AI.

• Seek context from other disciplines and incorporate diverse technical perspectives into recommendations.
• Be responsive and reliable in remote settings, maintaining momentum without requiring constant oversight.
• Adapt quickly to shifting priorities, new data, and evolving project scopes.
• Be comfortable with ambiguity and incomplete information, using sound engineering judgment to move decisions forward.

• B.S. in Engineering (Petroleum, Mechanical, Chemical, or related discipline).
• 2+ years of experience in reservoir engineering, data science, or a related technical field; a PhD may be considered in lieu of industry experience.
• Strong fundamentals in reservoir engineering, including fluid flow in porous media, pressure transient analysis, material balance, and production/injection performance analysis.
• Experience with reservoir modeling and simulation (numerical simulators, decline analysis, forecasting tools).
• Proficiency in analyzing subsurface datasets, including pressure, rate, temperature, and geologic data.
• Working knowledge of Python and scientific libraries (Num Py, Pandas, Sci Py) or similar analytical environments.
• Experience applying statistical analysis, data-driven modeling, or machine learning techniques to subsurface or production data.
• Ability to manage and integrate large, multi-disciplinary datasets.
• Strong problem-solving skills with the ability to translate technical findings into actionable insights.
• Excellent written and verbal communication skills.

• Experience applying machine learning or AI techniques to engineering or geoscience problems
• Experience in geothermal reservoir engineering, enhanced geothermal systems (EGS), or unconventional resource development.
• Hands-on experience building and calibrating numerical reservoir simulation models for thermal or multiphase systems.
• Proficiency in advanced Python-based data workflows, version control (Git), and reproducible modeling practices.
• Experience working in cloud or high-performance computing environments for large-scale simulations or data processing.
• Exposure to real-time data systems, digital twins, or automated…