ASIC Quality Data Analyst

Grow with us ASIC Quality Data Analyst Austin, Texas Hybrid, on-site work schedule Ericsson Silicon is expanding its custom ASIC capabilities for 5G Advanced and 6G. The Central Engineering ASIC Quality team supports product quality and reliability throughout the ASIC lifecycle. The analysis starts with factory failures and field returns and follows the evidence wherever it leads, including wafer fabrication, assembly, and test. Semiconductor expreience is required for this opening. We are looking for an ASIC Quality Data Analyst with strong AI and analytical capabilities to analyze factory and field failures, identify recurring failure signatures, and support root-cause investigations. The role brings together production test, manufacturing, repair, and field-return data to reduce the time needed to diagnose failures and establish root causes. The findings will also support test development and screening improvements that reduce defective parts per million (DPPM) and improve the quality of our products in the field. What you will do: You will own the analytical work supporting factory and field failure investigations, including data correlation, analytical tools, automated workflows, and reporting. You will work with internal factories, field organizations, Product Engineering, Test Engineering, Quality, and other engineering teams to develop evidence and evaluate failure hypotheses. Engineering and Quality owners remain responsible for confirming root causes, making containment decisions, and implementing corrective actions. You will also engage semiconductor fabs and external manufacturing partners as needed to trace failures across the supply chain and support corrective actions. Analyze factory production, test, repair, and field-return data to identify failure trends, recurring signatures, quality escapes, and emerging product risks. Work directly with internal factories and field organizations to collect and correlate failure evidence, prioritize analytical work with investigation owners, and support containment, root-cause analysis, and corrective actions. Collaborate with Product Engineering, Test Engineering, ASIC Design, Reliability, Quality, Manufacturing, Validation, and System Debug teams to investigate factory and field issues and validate technical findings. Develop and maintain tools, scripts, and analytical models that correlate factory and field data, identify recurring failure signatures, and prioritize hypotheses for engineering investigation. Build and maintain automated data pipelines linking factory test, manufacturing history, repair records, and field returns, with the traceability needed to follow failures back to production lots, devices, and processes. Apply statistical analysis and AI/ML techniques to detect abnormal failure patterns, group related returns, automate data analysis, and identify quality risks earlier. Use production yield, screening, defect, wafer fab, and assembly/test data to investigate factory and field failures, assess quality escapes, and evaluate the effectiveness of process or test changes. Develop dashboards and reporting tools to track factory failure rates, field returns, DPPM trends, recurring failure modes, and investigation progress, including time to diagnosis and root-cause confirmation. Document analytical findings, present supporting evidence, and work with engineering and factory teams to confirm root causes and measure the effectiveness of corrective actions and screening changes. Reduce the time needed to diagnose and root-cause factory and field failures by automating data preparation, correlating evidence across sources, and making relevant failure history available to investigation teams. Join our Team What you will bring: The ability to Identify failure modes contributing to factory defects and field escapes is critical in this role. Your experience providing failure signatures and analytical evidence to Product Engineering and Test Engineering to support test development and validation, reduce outgoing DPPM, and evaluate screening effectiveness, including escapes and false rejects is critical. Bachelor’s degree in Engineering, Computer Science, Data Science, Data Analytics, or a related field. 3–5 years of experience in data analytics, engineering analytics, data science, or a related role in a semiconductor environment. Hands-on experience with Python, SQL, data modeling, visualization, or comparable analytical tools. Practical experience using statistical methods and AI/ML techniques for data analysis or workflow automation. Strong analytical and problem-solving skills, including experience working with complex or inconsistent data from multiple sources. Ability to collaborate across engineering disciplines and communicate technical findings clearly. Experience in semiconductor manufacturing, factory test, product engineering, quality, reliability, failure analysis, or related hardware environments. Familiarity with manufacturing failure data, production test results, repair records, field returns, and device traceability; exposure to wafer fabrication or assembly processes is beneficial. Experience developing and maintaining analytical tools, automated workflows, anomaly-detection models, or predictive quality methods. Experience with LLMs, AI agents, retrieval-augmented generation (RAG), or related AI frameworks. Demonstrated experience using data to support failure investigations, improve screening, or reduce recurring manufacturing or field issues. What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like. Ericsson uses a merit-based hiring approach that values people with different experiences, perspectives and skillsets. We truly believe this approach drives innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson te