Reliability Engineering Manager
A world leader in the design and manufacture of electronic systems for the chemical industry is seeking a Reliability Engineering Manager, to join then their Headquarters & Global Technology Centre in Burgess Hill site. The company are a major global leading engineering company who are part of a business worth over £10 billion. They develop complex, high value, high speed machinery which is used in the manufacture of semiconductors.
As The Reliability Engineering Manager, Burgess Hill your responsibilities will include the provision and maintenance of the Design for Reliability process for NPI projects this will include measurement, tracking and reporting of critical product reliability data for use across entities. Also, to support sales with customers for example process ownership for FMEA and Peer Review and development of further reliability engineering best practices, this will be based on research and networking with a reliability peer group external to the business. You will report in to the Quality Director.
Success will rely on continued development and implementation of reliability engineering techniques and methods which will be planned and executed as business area wide projects with priorities set by the job holder and a reliance on change management and influencing skills to coordinate effectively with impacted parts of the business, understanding cultural differences across geographies and varying expectations between market sectors.
The Reliability Engineering Manager, Burgess Hill will be Degree level or higher in an engineering or science discipline. You will be fluent in English both written and verbally. Work based experience in product reliability engineering and significant experience in a multidiscipline, international engineering company. You will have knowledge and practical experience of FMEA, Weibull and RBD including software applications and experience of design of experiment, accelerated life testing and reliability lifetime predictions. You will be able to analyse and present data and facts concisely with a good level of quality statistical knowledge and experience.
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