Decarbonizing the energy sector is an urgent challenge, and with the right technology, geothermal energy can play a central role. We are expanding our team to help make this vision a reality. Fervo Energy has developed technology to make geothermal power scalable and cost-effective. Join us and work alongside world-class engineers, researchers, investors, and developers in making geothermal a key pillar of our climate-friendly energy future.
Description
As a Computational and Data Scientist, you will work on challenging engineering, geoscience, and commercial problems to support Fervo Energy’s geothermal projects across exploration, development, production, and electricity generation. In this role, you will be responsible for building and maintaining Fervo’s compute, networking, storage, and database architecture on our preferred cloud infrastructure. You will create edge-to-cloud platforms capable of processing terabytes of fiber optic, drilling, IoT, wellfield, and power plant data using advanced artificial intelligence and machine learning processing to improve Fervo’s geothermal operations. This position will work with a cross-disciplinary team of reservoir engineers, geophysicists, surface facilities engineers, and business analysts to provide solutions that deliver value to Fervo Energy.
Requirements
- Build and manage Fervo’s compute, networking, storage, and database architecture using the Google Cloud Platform
- Develop, deploy, and maintain a high-performing and user-friendly software stack for data analytics, machine learning, data management, I/O, containerization, data storage, data transfer, and other workloads
- Design, develop, and implement machine learning models, including data collection, preprocessing, feature engineering, model selection, and hyperparameter tuning
- Develop edge-to-cloud platforms to process and analyze fiber optic data, power plant data, and other data collected at Fervo’s geothermal project sites
- Develop software to visualize large geophysical data sets
- Evaluate, select, and configure virtual machines and bare metal servers from Google Cloud to optimize performance for high performance computing (HPC) workloads
- Develop, implement, and maintain security controls and protocols to ensure the confidentiality, integrity, and availability of data and systems in cloud-based environments, and to comply with industry best practices and regulatory requirements
- Collaborate with cross-functional teams to integrate and scale Fervo’s software and data infrastructure and ensure compatibility with other systems and platforms
- Stay up-to-date with emerging technologies, tools, and best practices related to artificial intelligence and machine learning software development, data management, and cloud infrastructure, and apply them to enhance Fervo’s capabilities and competitive advantage
Required & Preferred Qualifications
- Bachelor’s degree in computer science, data science, petroleum engineering, geophysics, or a related discipline; Master’s or Ph.D. degree a plus
- 5+ years of experience in developing and deploying large-scale computational and data science solutions on cloud platforms
- Familiarity with public cloud compute and storage resource orchestration (Google Cloud Platforms, AWS, Azure); Google Cloud technical certifications a plus
- Working knowledge of cluster deployment and operations on the cloud. Experience with containers – Docker, Kubernetes
- Experience working with, processing, and managing large data sets (multi-TB scale); Working knowledge with distributed fiber optic and other geophysical data a plus
- Experience in developing and maintaining software documentation using collaborative version control software (e.g. Git)
- Demonstrated proficiency in developing code in at least one language (ideally Python or C/C++)
- Preferred: Understanding of single-node performance (code optimization) and multi-node scaling issues for the data science portfolio (MPI, multi-threading, GPU, etc.)
- Preferred: knowledge of developing deep learning models using tools such as TensorFlow, Keras, PyTorch, etc. optimization
- Preferred: Ability to build MLOps pipelines on cloud solutions
- Preferred: Background in signal processing, geophysics, or petroleum engineering
- Preferred: Background in applied mathematics/physics, especially in inverse problems, numerical inversion, numerical simulation, linear and nonlinear solvers, and computational optimization
