Data Science Intern - Master's/ Ph.D

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 looking for a Data Science Intern to join our team in one of today’s most exciting technologies.  This role will report to Director, Service Business Intelligence and based in San Jose, CA.  This is a fully on-site, in office role.

 

Role and Responsibilities:

Designing and deploying Machine Learning algorithms for industrial applications such as predictive maintenance, and process optimization

Collaborate with cross-functional teams to deploy machine learning solutions to drive for optimum performance and reliability

Assist in collecting, analyzing, and interpreting data to identify trends, patterns, and opportunities for process improvement

Develop visually appealing and informative data visualizations to effectively communicate insights to stakeholders

Prepare and maintain documentation related to business processes, data analysis methodologies, and findings

 

Required Skills and Experience:

Current enrollment in Master’s or Ph.D Computer Science, Electrical Engineering, Data Science, Statistics, or a related field graduating by Summer 2026

This internship program is from June to August 2025

Applied Machine Learning experience (regression and classification, supervised, self-supervised, and unsupervised learning)

Strong mathematical background (linear algebra, calculus, probability, and statistics)

Proficiency in Python and objected-oriented programming, e.g. JavaScript

Familiarity with key Python packages for data wrangling, machine learning, and deep learning such as pandas, sklearn, tensorflow, torch, etc.

Experience with version control software, Github/Gitlab 

Ability to drive a project and work both independently and in a cross-functional team

Smart, motivated, can-do attitude, and seeks to make a difference in a fast-paced environment.

Excellent verbal and written communication

 

Preferred Qualifications:

Experience with scalable ML (MapReduce, Spark)

A portfolio of projects (GitHub, papers, etc.)

Energy industry class, research or work experience is a plus

 

Bloom Energy is an equal opportunity employer and makes employment decisions based on 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 based on disability. Bloom Energy makes reasonable accommodations, 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.

 

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.