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Sobre este puesto de RMCC Data Science Intern en Bloom Energy

Bloom Energy · Presencial · San Jose, California

This is a summer internship starting May/June and is fully on-site at the listed office location.


Company Overview

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 a 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.


Department Overview

Remote monitoring control center (RMCC) is responsible for monitoring, optimizing and supporting field services for entire fleet of over 30,000 fuel cells deployed worldwide.


Intern Project

Interns will have a primary project during the summer internship and may work on additional projects. While the project could change by the time of the internship, we anticipate that the main project will involve the following:


Analyze fuel cell operational data, validate existing degradation detection logic, improve detection accuracy, and develop automation to streamline the workflow from detection and validation through notification, tracking, and resolution.


Position Responsibilities

  • Analyze historical and real-time fuel cell operating data to identify degradation patterns and abnormal operating behavior.
  • Validate existing Fuel Cell Degradation Detection algorithms using known historical events and operational data.
  • Evaluate detection accuracy, including false positives, false negatives, precision, and recall.
  • Develop and optimize algorithms for earlier and more reliable identification of fuel cell degradation.
  • Use Python and statistical analysis to evaluate operating trends, thresholds, correlations, and anomalies.
  • Develop automated data pipelines and tools to continuously monitor fleet data for potential degradation conditions.
  • Automate workflows from detection → validation → notification → action tracking → resolution.
  • Develop dashboards and reporting tools to monitor detected conditions and algorithm performance.
  • Collaborate with RMCC operators and engineers to validate algorithm results against actual operating conditions.
  • Document algorithms, validation methodologies, automated workflows, and performance results.

Minimum Qualifications

  • Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, Computer Engineering, or a related field.
  • Must be graduating between spring 2027 and summer 2028

Desired Qualifications

  • Proficiency in Python.
  • Strong understanding of algorithms, data structures, and software development.
  • Experience with data analysis using Pandas, NumPy, or similar tools.
  • Strong analytical and problem-solving skills.
  • Ability to work with large datasets and translate operational requirements into software solutions.
  • Experience with SQL/NoSQL databases, REST APIs, Git, and data pipelines.
  • Knowledge of statistical analysis, anomaly detection, classification, or time-series analysis.
  • Familiarity with machine-learning libraries
  • Experience developing automation, monitoring applications, dashboards, or engineering tools.
  • Interest in energy systems, predictive analytics, reliability engineering, or AI/ML.
  • Ability to evaluate algorithm performance using measurable metrics and test datasets.

Visa Sponsorship  
Bloom Energy is unable to provide VISA sponsorship to interns or new grad hires on an F-1, J-1 or other visa for this position, regardless of CPT or OPT status.


EOE Statement
Bloom Energy is an equal opportunity employer and makes employment decisions on the basis of merit. We are committed to compliance with all applicable laws providing equal employment opportunities. All qualified applicants will receive consideration for employment without regard to race, sex, color, religion, national origin, protected veteran status, or on the basis of disability. Bloom Energy makes reasonable accommodation, consistent with applicable laws, for the known physical or mental limitations of an otherwise qualified applicant or employee with a disability, who can perform the essential job functions, unless undue hardship would result.


Salary Statement with pay range
Bloom Energy is committed to fair and equitable compensation practices. The total compensation for this position is based on various factors including, but not limited to, relevant skills and experience.


The pay range for this position is $36-$50 per hour.

Salary Ranges:

$36.00 - $39.00
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Sobre Bloom Energy

Bloom Energy's mission is to make clean, reliable energy affordable for everyone in the world. With roots in NASA’s Mars Program, the company’s founder, chairman, and Chief Executive Officer, Dr. K.R. Sridhar, developed an innovative fuel cell technology that is shifting the energy paradigm and redefining the electric power market. The Bloom Energy Server is a distributed electric power solution built for the digital age and capable of delivering highly reliable, uninterrupted, 24x7 constant power that is also clean and sustainable. Bloom’s unique on-site power generation systems are among the most efficient on the planet, providing significantly reduced operating costs and producing dramati

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