Reliability Engineer

Job Description

( Innovation Center. Eatons research lab focuses on the front end of innovation by: conducting research to advance the state of the art, emphasizing cross-business impact technologies, and innovating to provide Eaton s customers with safe, efficient, and reliable power management solutions. This role is established for Automotive business of Eaton in Center for Reliability & Safety team. The core responsibilities of this position are following)

• Responsible for delivering DFR work packages across multiple New Product Introduction (NPI) and Sustaining Engineering (SE) projects of vehicle product lines with high quality and meeting project schedules.
• Responsible to identify and mitigate reliability risks under guidance & follow reliability sign-off process for NPIs.
• Responsible for doing warranty data analysis,forecasting, publishing monthly warranty reports and driving problem solving for top warranty issues.
• Responsible for doing deep warranty data analysis using primary data science tools and deriving inferences to support business decisions
• Create and/or revise DFR deliverables such as Reliability goal setting, Reliability Predictions, Failure Mode Effects Analysis (FMEA), Accelerated Life Test , Reliability Demonstration Test (RDT), Reliability Growth analysis ,FRACAS for NPIs, VAVE projects and/or existing product improvements.
• Work with cross functional team as well as global reliability engineering team for designing in reliability on NPI programs and/or existing product improvements,VAVE projects & responsible for identifying, managing and mitigating reliability risks under supervision of senior.
• Planning project milestones, executing milestones,conducting deliverables reviews, communication with stakeholder and on-job development through technical mentoring.
• Along with warranty monitoring, forecasting, reporting; develop actuarial models library, creating warranty Power BI dahsboards for vehicle components
• Doing deep warranty data analysis using primary data science tools and deriving inferences to support business decisions
• Create, publish, and maintain work instructions for current product standard work ensuring best practices are shared across the product lines
• Responsible for on-time delivery of current product reliability engineering tasks with assured quality

QualificationsRequirement:

• Required: Bachelor s Degree in Mechanical Engineering.
• Preferred: Master s Degree in Reliability Engineering.
• Master s Degree in Reliability Engineering with 0-1 year experience in Automotive Reliability
• Bachelor s Degree in Mechanical Engineering with 3-4 year experience in Automotive Reliability
• Skilled in the areas of reliability statistics, FMEA, FTA,8D,FRACAS, life modelling & analyzing field/warranty data into relevant life and failure rate information, data science methods
• Experience with any industry proven reliability analysis suite of tools like Minitab, Reliasoft preferred, programming tools like python preferred
• Desired: Work experience in the Automotive systems preferably transmission, valve train, supercharger, differntials etc.
• Preferred: DFSS GB certified from reputed organization etc.
• Process Management-Good at figuring out the processes necessary to get things done, knows how to organize people and activities, knows what to measure and how to measure it, Can simplify complex processes, Gets more out of fewer resources
• Problem Solving - Uses rigorous logic and methods to solve difficult problems with effective solutions, probes all fruitful sources for answers, can see hidden problems, Is excellent at honest analysis Looks beyond the obvious and doesnt stop at the first answers
• Decision quality makes good decisions based upon a mixture of analysis, wisdom, experience, and judgment
• Drive for results can be counted on to exceed goals successfully
• Interpersonal savvy relates well to all kinds of people; builds appropriate rapport.

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Keyskills :
reliabilityfmeamechanicalsafetycommissioningnew product introductionpower bifront enddata sciencedata analysisstatements of work sow