Data Analytics & Management, Technical Operations Management

We are looking for a highly motivated Principal / Senior Engineer, Data Analytics & Management to join our growing Technical Operations Management (TOM) organization. The role will drive manufacturing intelligence, test analytics, quality screening, analytics platform development, digital transformation, and operational excellence across the semiconductor product lifecycle.

Candidates with strong knowledge in semiconductor data analytics and tools, outlier detection methodology (e.g.: PAT, SBL, cluster defect analysis) and data infrastructure setup is highly preferred.

Key Responsibilities

Test Analytics & Quality Screening

• Own the test analytics strategy covering Wafer Sort (WS), Burn-In (BI) and Final Test (FT).
• Develop advanced analytics solutions for yield learning, outlier detection, excursion monitoring, statistical screening, and quality monitoring.
• Support implementation and optimization of quality screening methodologies including PAT, Statistical Bin Limits (SBL), dynamic guard-banding, clustering, and test escape prevention.
• Collaborate with Product Engineering and Test Engineering teams to improve outgoing quality performance and reduce DPPM.

Analytics Platform Ownership

• Own the manufacturing analytics platform ecosystem, e.g. JMP, Power BI, Databricks, and yield related tools.
• Define platform roadmap, governance, deployment strategy, and user adoption plans.
• Establish best practices for analytics development, data governance, reporting standards, and engineering productivity.
• Provide technical leadership for tool evaluations, vendor engagement, implementation, and user training.

Fabless Operations Systems & Tool Setup

• Define system requirements and integration strategies between Foundries, OSATs, MES, Quality Management Systems, and analytics platforms.
• Drive standardization of manufacturing data flows, system interfaces, and engineering workflows.
• Develop operational tools to support NPI, qualification, production ramp, and mass production activities, e.g. process release, deviation material management, change management.

Data Infrastructure & Automation

• Own end-to-end semiconductor manufacturing data ecosystem across wafer, assembly, test and quality operations.
• Design and manage manufacturing data solutions, including databases, ETL pipelines, dashboards, cloud architectures, and automated reporting systems.”
• Develop automation solutions using Python, SQL, Power BI, VBA, and modern analytics technologies to improve engineering productivity.