Sr. Staff Data Scientist

At Bloom Energy, our vision for a world powered by clean, reliable, and affordable energy is more than just a dream—we’re making it reality.

For over two decades, we’ve been at the forefront of the global energy transition, pioneering solutions that empower critical industries to thrive in a rapidly digitizing, energy-intensive world. From revolutionizing power for AI-driven data centers to ensuring resilience for hospitals, electric grids, manufacturing facilities, and utilities, our solid oxide fuel cell (SOFC) and solid oxide electrolyzer (SOEC) technologies are redefining what’s possible by delivering energy abundance for all.

With more than 30,000 fuel cell modules deployed worldwide, we are the trusted partner for Fortune 100 companies and innovators alike. Our cutting-edge solutions enable unparalleled “time-to-power” capabilities, reliability, and sustainability, ensuring our customers remain ahead in a world where soaring energy demand and intensifying energy scarcity are rapidly becoming the new norm.

At Bloom, we thrive on collaboration, bold thinking, and relentless innovation. We believe that, together, we can create a brighter, more sustainable future while tackling the most pressing challenges of the 21st century.

We are seeking a Sr. Staff Process Data Scientist to join our Data Science team, where you will apply deep chemical engineering expertise alongside coding, analytics, and automation to improve, scale, and optimize complex manufacturing processes.
This role sits at the intersection of process engineering, data science, and software, focusing on building scalable, data-driven solutions rather than day‑to‑day operations.

This role will report to Sr. Principal Engineer, Data Science and is based in San Jose, CA.

This is a fully on-site, in office role 5 days a week.

Key Responsibilities

• Design and develop Python-based tools, pipelines, and automated workflows
  for engineering analysis
• Build, deploy, and maintain digital twins, soft sensors, and advanced analytics models
  for process optimization
• Analyze large-scale manufacturing datasets
  (including time-series and historian data) to identify opportunities in yield, throughput, and reliability
• Partner cross-functionally with process engineers, data scientists, and software engineers
  to operationalize solutions in production environments
• Translate complex process engineering challenges into scalable data models and software implementations
• Own ambiguous, high-impact problems and drive them from model concept through validation, deployment, and monitoring

Required Qualifications

• MS or PhD in Chemical Engineering, Mechanical Engineering, Electrical Engineering, or a related field in the physical sciences (e.g., Physics, Chemistry, Applied Mathematics)
• 6+ years of industry experience
  , with demonstrated impact at a senior or staff level
  in industrial, manufacturing, or process-oriented environments
• Strong ability to translate physical systems and engineering/scientific problems into data-driven models and production-grade code
• Solid foundation in first principles, physical systems, and process understanding
  , with the ability to connect theory to real-world applications
• Experience working with complex systems involving sensors, instrumentation, or process data

Core Skills

• Process modeling & engineering fundamentals
  • First-principles modeling, scale-up, and root-cause analysis
• Programming & data analysis
  • Python (Num Py, Pandas, Sci Py, visualization libraries)
  • Automation of engineering calculations and analytical workflows
• Data engineering & analytics
  • Large-scale datasets, time-series analysis, and process historian data
• Machine learning for physical systems
  • Statistical modeling, hybrid modeling (physics + ML), or ML applied to process optimization
• Problem-solving & ownership
  • Ability to operate in ambiguous environments and deliver end-to-end solutions

Nice-to-Have (Optional but Valuable)

• Experience with digital twin platforms or industrial AI frameworks
• Familiarity with cloud environments (Azure, AWS, or GCP)and MLOps pipelines
• Experience deploying models into real-time or near real-time production systems
• Knowledge of semiconductor, chemicals, energy, or advanced…