Director, System Performance & Reliability Analytics

Bloom Energy•San Jose, CA

79d•Onsite

About The Position

Our team at Bloom Energy embraces the unprecedented opportunity to change the way companies utilize energy. Our technology empowers businesses and communities to responsibly take charge of their energy. Our energy platform has three key value propositions: resiliency, sustainability, and predictability. We provide infrastructure that is flexible for the evolving net zero ecosystem. We have deployed more than 30,000 fuel cell modules since our first commercial shipments in 2009, sending energy platforms to data centers, hospitals, manufacturing facilities, biotechnology facilities, major retail stores, financial institutions, telecom facilities, utilities, and other critical infrastructure customers around the world. Our mission is to make clean, reliable energy affordable globally. We never stop striving to improve our technology, to expand and improve our company performance, and to develop and support the many talented employees that serve our mission! Bloom Energy is seeking a visionary and strategic leader to serve as Director, System Performance & Reliability Analytics. This role will lead the development and execution of advanced analytics frameworks to optimize the performance, reliability, and operational efficiency of our global fuel cell fleet. The ideal candidate will bring deep technical expertise, strong leadership capabilities, and a passion for leveraging data to drive innovation and continuous improvement. This role will report to Head of Quality and Reliability and is based in San Jose, CA. This is a fully on-site, in office role.

Requirements

Bachelor’s or Master’s degree in Data Science, Mechanical, Electrical, Chemical, Reliability, or Systems Engineering; advanced degree preferred.
12+ years of experience in product reliability, performance analytics, or systems engineering.
Proven track record of building and leading high-performing teams across technical and business functions.
Expertise in data mining and analytics using Python, SQL, and statistical tools (JMP, Minitab); Tableau experience preferred.
Strong understanding of reliability engineering methodologies including Fault Tree Analysis, Reliability Block Diagrams, and Markov modeling; experience with Reliasoft or equivalent tools is a plus.
Skilled in RCCA methodologies (Six Sigma, 8D) with experience solving complex problems and driving corrective actions.
Strong business acumen with the ability to translate technical insights into strategic initiatives.
Exceptional communication and presentation skills, with the ability to influence senior leadership and guide cross-functional alignment.
Visionary mindset with a passion for leveraging data to drive innovation, operational excellence, and long-term product reliability.
Strategic Thinking & Vision Setting
Cross-Functional Collaboration
Innovation & Continuous Improvement
Data-Driven Decision Making
Executive Communication & Influence
Operational Excellence

Responsibilities

Lead the strategic direction and execution of fleet performance analytics, utilizing the reliability performance models, and field data to drive predictive insights and early anomaly detection.
Define and communicate a clear vision for data-driven reliability engineering, aligning cross-functional teams and fostering a culture of innovation and continuous improvement.
Define and execute strategies for utilizing fleet data that reduces overall service cost by enabling predictive product failure using deep learning algorithms to identify anomalous behavior that is correlated to the physics of the product failure modes and degradation.
Lead and participate in the engagement with C3 AI during the 2 year Reliability partnership.
Develop and implement advanced alerting systems and visual dashboards to monitor regional, product-specific, and operational deviations.
Integrate data from diverse sources to identify systemic degradation patterns and inform strategic reliability initiatives.
Oversee data validation and analysis using Python, SQL, and statistical tools to ensure high-quality insights and support executive decision-making.
Collaborate with Engineering, Quality, Field Service, and AI teams to accelerate root cause investigations and corrective actions.
Champion automation of analytical workflows to enable scalable and efficient fleet monitoring.
Facilitate the execution of automated inspection through pattern detection from learning models to improve efficiency on the production floor and reduce quality escapes in station.
Monitor and report on cost-of-poor-quality metrics, driving initiatives to reduce inefficiencies and improve product lifecycle economics.
Lead enterprise-wide problem-solving efforts, facilitating collaboration across Service, Operations, Engineering, and Quality teams.
Deliver high-impact presentations to internal and external stakeholders, translating complex technical analyses into strategic recommendations.