Sr. Predictive Analytics Engineer - Operational Excellence

• to join our team in one of today's most exciting technologies. This role will be based in

• Provide engineering and technical guidance to promote the achievement of all reliability, quality, cost and delivery goals for all components.
• Provide support to the Quality Engineering, Supply Chain Quality, Manufacturing Quality, and PCS Quality teams. Apply the existing policies and procedures to achieve zero-defect flow ("dock-to-stock") for all purchased, contract manufacture parts, materials, and Bloom Energy manufactured parts.
• Measure and categorize quality costs into prevention, appraisal, internal failure, and external failure metrics using data from ERP and quality management systems.
• Drive component and design failures back to the earliest point of detection in the process.
• Work with the product engineering team to teach and perform early stage risk assessments (DFMEA et.al.) and establish reliability testing programs for all components; electrical, mechanical, electromechanical, and raw materials to meet life-in-service business plans.
• Responsible for Cost of Quality monitoring and tracking. Ensure accuracy of the data and work with each functional owner to break down cost into second, third, and fourth level paretos with root cause analysis
• Initiate, support and/or lead activities related to quality, continuous process improvement, qualification, and reduction in cost of poor quality.
• Lead critical problem solving task forces with emphasis on cross functional inputs, accountability, time to RCCA, and ensurance of mitigation of inventory, or field units at risk.
• Perform data-driven root cause failure analyses for design, manufacturing, and administrative problems.
• Demonstrate the ability to interface and work collaboratively across departments internally and with suppliers and customers, (including Purchasing, Quality, R&D, Manufacturing, Product Engineering, Marketing and Finance), while building strong and cooperative working relationships.

• 7+ years of increasing responsibility in the areas of Quality, Manufacturing and Reliability.
• Proven successful application of multi-discipline engineering problem solving techniques.
• Understand the critical elements of modern Manufacturing Statistical Process Control, (e.g., practical working knowledge and experience with SPC, Process Capability, Cp, Cpk, etc.)
• Strong verbal/active listening/written communications.
• Demonstrate planning and leadership skills.
• Familiarity with metrology, calibration, and Gage R & R's.
• Experience in Quality System Auditing.
• Experience in product phase-gate, NPI and technology transfer.
• Experience applying PPAP, First Article Inspections (FAI) and Reports).
• Strong critical thinking, analytical skills, and problem solving.
• Drive material quality improvement through supplier manufacturing process using incoming and in-process data gathering and analysis.
• Ability to effectively work within a very intense, dynamic, fast-paced, and demanding work environment.
• Proficiency in the use of Microsoft Office Suite and statistical software.
• Possess a passion for excellence.
• BS, MS or PhD in Mechanical/Material Science/Chemical/Electrical/Systems Engineering.

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 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 includes standard company benefits and is based on various factors including, but not limited to, relevant skills and experience.