Sr. Predictive Analytics Engineer - Operational Excellence

Bloom Energy•San Jose, CA

14d•$155,800 - $224,200•Onsite

About The Position

Bloom Energy is seeking a Sr. Predictive Analytics Engineer for its Operational Excellence team. This fully on-site role is based in San Jose, California, and supports reliability, quality, cost, and delivery goals across components and manufacturing operations. The position provides technical guidance to Quality Engineering, Supply Chain Quality, Manufacturing Quality, and PCS Quality teams, applying policies and procedures to achieve zero-defect flow for purchased, contract-manufactured, and Bloom Energy-manufactured parts and materials. The engineer measures and categorizes quality costs using ERP and quality management system data, drives component and design failures to the earliest point of detection, and works with Product Engineering on early-stage risk assessments and reliability testing programs. The role monitors Cost of Quality, validates data, performs root-cause analyses, and leads or supports quality improvement, qualification, and cost-of-poor-quality reduction initiatives. It also leads critical problem-solving task forces and collaborates across internal functions, suppliers, and customers. The role requires at least 10 years of increasing responsibility in quality, manufacturing, and reliability, along with experience in multidisciplinary engineering problem solving, statistical process control, quality systems, product phase-gate/NPI, technology transfer, PPAP, and first article inspections. A BS, MS, or PhD in a specified engineering discipline is required. The stated salary range is $155,800 to $224,200. Compensation also includes standard company benefits, with total compensation based in part on relevant skills and experience.

Requirements

10+ years of increasing responsibility in Quality, Manufacturing, and Reliability.
Proven successful application of multidisciplinary engineering problem-solving techniques.
Practical working knowledge and experience with Statistical Process Control, process capability, Cp, and Cpk.
Strong verbal, active listening, and written communication skills.
Planning and leadership skills.
Familiarity with metrology, calibration, and Gage R&R.
Experience in Quality System Auditing.
Experience with product phase-gate, NPI, and technology transfer.
Experience applying PPAP and First Article Inspections and Reports.
Strong critical thinking, analytical, and problem-solving skills.
Ability to drive material quality improvement through supplier manufacturing processes using incoming and in-process data gathering and analysis.
Ability to work effectively in an intense, dynamic, fast-paced, and demanding environment.
Proficiency with Microsoft Office Suite and statistical software.
Passion for excellence.
BS, MS, or PhD in Mechanical, Materials Science, Chemical, Electrical, or Systems Engineering.

Responsibilities

Provide engineering and technical guidance to achieve reliability, quality, cost, and delivery goals for components.
Support Quality Engineering, Supply Chain Quality, Manufacturing Quality, and PCS Quality teams in pursuing zero-defect flow for purchased, contract-manufactured, and Bloom Energy-manufactured parts and materials.
Measure and categorize quality costs into prevention, appraisal, internal failure, and external failure metrics using ERP and quality management system data.
Drive component and design failures back to the earliest point of detection in the process.
Work with Product Engineering to teach and perform early-stage risk assessments, including DFMEA, and establish reliability testing programs for components and raw materials.
Monitor and track Cost of Quality, ensure data accuracy, and work with functional owners on detailed Pareto breakdowns and root-cause analysis.
Initiate, support, or lead quality, continuous process improvement, qualification, and cost-of-poor-quality reduction activities.
Lead critical problem-solving task forces, emphasizing cross-functional input, accountability, time to RCCA, and mitigation of inventory or field units at risk.
Perform data-driven root-cause failure analyses for design, manufacturing, and administrative problems.
Collaborate across internal departments and with suppliers and customers, building strong working relationships.