Staff Product Engineer Data Analytics

Analog Devices•Milpitas, CA

2d•$150,978 - $218,624

About The Position

The Staff Product Engineer Data Analytics supports new product introduction (NPI), qualification, production ramp, and high-volume manufacturing of power management ICs. The role uses large-scale manufacturing, test, yield, reliability, and process data to improve product quality, operational excellence, and manufacturing yield. It works cross-functionally with design, test, quality, operations, foundry, and OSAT partners to identify trends, investigate root causes, develop insights, and drive corrective actions through data-informed decisions. The position also supports manufacturing readiness and program execution, maintains product and qualification documentation, and develops predictive analytics and monitoring approaches to identify manufacturing risks proactively. The role is intended for an analytical, technically proficient engineer who can work independently in a fast-paced, multidisciplinary environment.

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 effective in a collaborative team environment.
Applicants who require access to technical data may be subject to an export licensing review, except U.S. citizens, U.S. permanent residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3).

Responsibilities

Own and manage product, test, yield, reliability, and manufacturing data throughout the product lifecycle, 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 support yield enhancement, quality improvement, and cycle-time reduction.
Develop automated data pipelines, dashboards, and reporting solutions 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.
Work with design, test, quality, operations, foundry, and OSAT teams to identify correlations among test results, process metrics, and product performance.
Support product qualification, characterization, production ramp, and sustaining engineering through data 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, presenting findings and improvement opportunities to engineering and management teams.
Support program management through 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 opportunities to improve 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 methodologies to proactively identify manufacturing issues and improve product performance.