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Senior Reliability Engineer
- Perform life data analysis using the Weibull distribution to evaluate product reliability and predict failure behavior.
- Analyze time-to-failure data from laboratory testing, accelerated life testing (ALT), HALT, HASS, and field returns to identify reliability trends and failure patterns.
- Develop reliability models, perform system-level reliability assessments, and apply reliability growth methodologies to track and improve product performance.
- Conduct reliability risk assessments using Failure Mode and Effects Analysis, DFMEA, and Fault Tree Analysis to identify potential failure modes and mitigation strategies.
- Lead root cause investigations and failure analysis for anomalies identified during testing or field performance.
- Clean, organize, and analyze reliability datasets, generate metrics (MTBF, MTTF, B10 life), and communicate insights to engineering teams and management.
- Collaborate with design, quality, and manufacturing teams to improve product reliability throughout the development lifecycle.
Qualifications:
- Typically requires a minimum of 5 years of related experience with a Bachelor’s degree; or 3 years and a Master’s degree; or a PhD without experience; or equivalent work experience.
- Strong knowledge of reliability engineering principles, life data analysis, and product failure mechanisms.
- Experience performing Weibull analysis and applying statistical distributions such as Weibull distribution, exponential, and lognormal.
- Experience with reliability risk assessment methods (FMEA, DFMEA, FTA) and executing reliability testing including ALT, HALT, and HASS.
- Strong statistical analysis and data interpretation skills with experience analyzing time-to-failure datasets and reliability metrics.
- Experience with reliability and statistical software tools such as ReliaSoft Weibull++, Ansys Sherlock, Minitab, and programming/analytical tools such as Python, R, or MATLAB.
- Strong analytical, problem-solving, organizational, and communication skills with the ability to present technical findings and reliability insights to cross-functional teams and management.
- Strong analytical and problem-solving skills with the ability to interpret complex reliability and statistical data.
- Detail-oriented and methodical, able to identify patterns, anomalies, and failure trends in datasets.
- Excellent technical communication skills, capable of presenting reliability findings, risks, and recommendations clearly to cross-functional teams.
- Collaborative mindset, working effectively with design, quality, manufacturing, and test engineering teams.
- Proactive and data-driven, able to identify potential reliability risks early in the product lifecycle and recommend mitigation strategies.
- Organized and able to manage multiple reliability assessments, testing activities, and projects simultaneously.
- Continuous learner, staying updated on emerging reliability engineering methods, statistical techniques, and analytical tools.
