The Reliability Engineer (Data Analytics) will apply reliability testing, statistical modeling, design of experiments (DOE), and data analytics to improve cable backplane assemblies for AI and hyperscale data centers. Working across design, manufacturing, and quality teams, this role will translate test, process, and field data into actionable insights that reduce risk, strengthen product robustness, and improve launch readiness. This position is based in Etters, PA; Raleigh, NC; or Nashua, NH. Some remote work may be considered based on the candidate’s qualifications. Approximately 30% travel is expected.
RESPONSIBILITIES:
- Develop data-driven reliability and robustness strategies across the product life cycle.
- Analyze reliability, manufacturing, FRACAS, RMA, and field data to identify trends, failure patterns, and systemic risks.
- Apply regression, ANOVA, hypothesis testing, distribution analysis, and multivariate methods to support risk-based decisions.
- Design and analyze experiments to identify critical factors and optimize product and process performance.
- Lead Six Sigma and structured problem-solving efforts using DMAIC, DOE, MSA, SPC, 8D, and root-cause analysis.
- Build reliability and life models using Weibull, Arrhenius, and physics-of-failure methods; correlate accelerated test results with field performance.
- Automate analyses and dashboards that communicate reliability metrics, variation, leading indicators, and emerging risks.
- Lead failure analysis and present clear technical conclusions and recommendations to cross-functional, executive, and customer stakeholders.
QUALIFICATIONS:
- Bachelor’s degree in engineering, reliability, statistics, data science, or a related quantitative field; advanced degree preferred.
- 5+ years of experience in reliability, validation, quality, data analytics, or failure analysis; cable, connector, or high-speed interconnect experience preferred.
- Proficiency in reliability statistics, life-data and accelerated-test modeling, sampling methods, confidence bounds, and regression.
- Experience with Six Sigma methods and tools; Green Belt or equivalent practical experience preferred.
- Proficiency with JMP, Minitab, Python, R, SQL, MATLAB, or comparable analytics and visualization tools.
- Hands-on experience with electrical, mechanical, and environmental testing and with diagnosing mixed failure modes.
- Strong analytical, technical leadership, and communication skills.
- Ability to travel up to 30%
Preferred Qualifications:
- Experience with HALT, HASS, ALT, ORT, and ESS methodologies.
- Experience with AI data centers, hyperscale infrastructure, or high-speed copper and hybrid cable technologies.
- Experience with predictive analytics, machine learning, anomaly detection, or reliability data pipelines.
- Familiarity with Manufacturing Execution Systems (MES) and manufacturing data integration, traceability, and analytics.
- Six Sigma Black Belt, Certified Reliability Engineer, or comparable certification.
- Familiarity with IPC, IEC, Telcordia, or MIL-STD standards and FIT, MTBF, warranty, or return-rate analytics.