Reliability strategy implementation for liquid cooling infrastructure equipment • Spearheaded the reliability strategy for New Product Introduction (NPI) of liquid cooling infrastructure, ensuring operational stability for next-generation data center equipment. • Executed comprehensive DFMEA that successfully identified and mitigated 20% of critical risks stemming from material incompatibility prior to deployment. • Orchestrated extensive material and liquid compatibility testing to establish technical benchmarks, directly informing the strategic selection of tier-1 component vendors. • Validated critical components through rigorous reliability testing, driving engineering changes with suppliers that realized $3M in cost avoidance by preempting potential field failures. • Optimized vendor manufacturing processes by integrating data-driven insights from reliability and material analysis, significantly enhancing product yield and consistency

DFR for New Product Introduction of EUV top module and alignment sensor at different Key Decision stages. • Drove cross-functional reliability growth for legacy and new product lines, reducing projected field failures by 20% • Embedded DfR methodologies across the engineering organization during the design phase, reducing late-stage design changes by 15%. • Designed and executed rigorous stress-testing protocols (HALT, HASS, ESS, ALT), decreasing time-to-market for reliability qualification by 5 weeks. • Researched and implemented 2 new predictive testing frameworks, directly increasing the organization's overall reliability competence and forecasting accuracy. • Pioneered novel predictive reliability models and experimental methodologies to elevate organizational reliability competence Qualification of new grease on the ball screws of Leg z-drives on RED module of Reticle Handler • Diagnosed root causes of systemic field failures by aggregating machine data and identifying grease degradation (outgassing) as the primary failure mode. • Performed statistical distribution modeling on field data to forecast failure parameters and formulated reliability estimates for a 7-year product design life. • Resolved failure risk and qualified new material solutions by collaborating with design teams to execute comparative Reliability Demonstration Testing (RDT) on legacy versus replacement grease.

Lifecycle DFR Implementation: Wind Turbine Blade NPD • Accelerated wind turbine blade manufacturing cycle time by 5% by developing and executing advanced Design of Experiments (DoE) methodologies. • Quantified warranty cost exposure by analyzing field failure data and translating customer design requirements into actionable reliability targets. • Steered risk management throughout New Product Development (NPD) by facilitating cross-functional DFMEA sessions across all major stage gates. • Validated product manufacturability by utilizing Minitab and R to construct and analyze statistical process and product capability charts. • Ensured precision in quality control systems by conducting Measurement System Analysis (MSA) and Gage R&R studies to verify UT scanner calibration. Risk Assessment on blade transfer and new technology implementation • Developed predictive statistical and regression models to evaluate serial production vulnerabilities, successfully mitigating blade transfer risk by 30%. • Leveraged R and advanced statistical methodologies to evaluate new technologies, driving measurable improvements in wind turbine blade reliability and overall longevity.

Product Reliability & Lifecycle Optimization • Extended product design life by 10% and accelerated time-to-market by implementing rigorous DMAIC continuous improvement strategies and engineering optimized DRT protocols to slash testing durations. • Eliminated critical field failures and complex design roadblocks across diverse systems (MOCVD modules, automation equipment) by translating field data and client requirements into actionable Design for Reliability (DFR) upgrades. • Forecasted and maximized product success rates by validating system architectures through advanced predictive modeling and the development of comprehensive functional diagrams. • Directed end-to-end reliability operations, executing full-cycle ownership of statistical model setup, project budget management, verification, and validation (V&V) processes.