Staff Product Engineer Data Analytics

Analog Devices•Milpitas, CA

About The Position

Analog Devices is seeking an experienced Product Engineer with strong semiconductor manufacturing data analysis expertise. The role supports new product introduction (NPI), qualification, production ramp, and high-volume manufacturing of power management ICs. It uses large-scale manufacturing, test, yield, reliability, and process data to improve product quality, operational performance, and yield. The engineer works cross-functionally with design, test, quality, operations, foundry, and OSAT partners to identify trends, determine root causes, and drive corrective actions through data-driven decision making. The position owns and manages product, test, yield, reliability, and manufacturing data throughout the product lifecycle. It develops analytics, automated pipelines, dashboards, and reporting to monitor manufacturing performance, identify risks, support product and process investigations, and guide qualification, ramp, and sustaining engineering. The role also contributes to manufacturing performance reviews and program management activities, maintains technical and qualification documentation, and identifies opportunities to improve manufacturing efficiency, throughput, automation, and equipment effectiveness. This position is for an experienced candidate and requires 10% travel. It is a first-shift/day-shift role. The expected wage range is $150,978 to $218,624, with the actual offer depending on factors such as work location, experience, education, and training. The position qualifies for a discretionary performance-based bonus.

Requirements

Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Data Engineering, Computer Science, Statistics, or a related technical field.
5–8+ years of experience in semiconductor product engineering, manufacturing engineering, yield engineering, NPI, or semiconductor data analytics.
Strong experience analyzing semiconductor manufacturing data, including wafer sort, final test, yield, reliability, assembly, and process control data.
Proficiency with data analytics and visualization tools such as Python, SQL, JMP, Spotfire, Excel, or similar platforms.
Experience developing automated reporting solutions, dashboards, and large-scale data analysis workflows.
Knowledge of statistical methods including DOE, SPC, regression analysis, correlation analysis, hypothesis testing, and trend analysis.
Hands-on experience working with semiconductor foundries, OSATs, and cross-functional engineering teams.
Strong understanding of semiconductor manufacturing flows, product qualification, reliability methodologies, and production ramp activities.
Demonstrated ability to drive yield improvement, defect reduction, root-cause analysis, and manufacturing optimization using data-driven approaches.
Excellent communication skills and ability to present complex data and technical findings to engineering, operations, and executive stakeholders.
Self-motivated, highly analytical, able to work independently, and comfortable in a collaborative team environment.

Responsibilities

Own and manage product, test, yield, reliability, and manufacturing data from NPI through high-volume production.
Analyze wafer sort, final test, assembly, and reliability data to identify yield issues, process excursions, and improvement opportunities.
Perform statistical analysis, data mining, and visualization of manufacturing datasets to improve yield, quality, and cycle time.
Develop automated data pipelines, dashboards, and reports to monitor yield, quality, reliability, WIP, cycle time, and production efficiency.
Lead root-cause investigations using statistical methodologies and engineering analysis to resolve product, process, and manufacturing issues.
Collaborate with design, test, quality, operations, foundry, and OSAT teams to correlate test results, process metrics, and product performance.
Support product qualification, characterization, production ramp, and sustaining engineering with data-driven analysis and recommendations.
Monitor process and yield trends across manufacturing sites and suppliers, identifying risks and escalation paths before production is affected.
Lead silicon and manufacturing performance reviews and present technical findings and improvement opportunities to engineering and management teams.
Support program management, including milestone planning, manufacturing readiness reviews, risk assessment, resource coordination, and cross-functional alignment.
Maintain product documentation, qualification reports, yield summaries, technical specifications, and manufacturing analytics reports.
Identify and implement data-driven improvements to manufacturing efficiency, throughput, automation, and overall equipment effectiveness.
Monitor WIP, cycle time, yield, and defect metrics to drive process optimization and operational excellence.
Develop predictive analytics and monitoring methods to proactively identify manufacturing issues and improve product performance.