Job Description

Accountabilities:

• Design and develop quality screening strategies, business rules, and disposition plans for centralized quality systems.
• Configure and maintain automated lot-hold, disposition, and containment workflows, ensuring robust controls and interlocks are in place to minimize quality escapes and operational risks.
• Collaborate with cross-functional stakeholders to establish risk-based quality control methodologies and validate enhancements to quality systems.
• Evaluate wafer lots affected by planned or unplanned manufacturing deviations and determine appropriate dispositions based on electrical test data, physical characterization, process history, and statistical analysis.
• Interpret outlier signals, defect signatures, excursion indicators, and other quality signals to support disposition decisions related to manufacturing excursions, quality deviations, fab conversions, and special work requests.
• Document disposition decisions and provide clear technical rationale to relevant stakeholders.
• Analyze large manufacturing datasets to identify abnormal signatures, trends, and emerging quality risks.
• Develop and apply statistical methodologies for outlier detection, screening optimization, excursion monitoring, distribution analysis, SPC review, baseline trending, and risk assessment.
• Create dashboards, metrics, and reporting solutions that improve operational visibility and support data-driven decision-making.
• Support AI, machine learning, predictive analytics, and other digital transformation initiatives aimed at automating quality monitoring and disposition processes.
• Partner with software development teams to define requirements and deploy scalable automation, workflow, and decision-support solutions.
• Work closely with site quality teams and global stakeholders to align quality execution, share best practices, and standardize processes across manufacturing locations.
• Support root-cause investigations and corrective/preventive action (CAPA) activities, contributing technical analysis and recommendations.
• Communicate technical findings, recommendations, risks, and business impacts clearly to technical and non-technical stakeholders.
• Develop and maintain operational procedures, business process documentation, training materials, quality metrics, and performance indicators.
• Lead or participate in cross-functional continuous improvement initiatives focused on productivity, quality, automation, and operational excellence.
• Support audits, compliance reviews, and quality system assessments while driving greater consistency and scalability across centralized quality operations.

Requirements:

• Bachelor's degree in Electrical Engineering, Electronics Engineering, Materials Science, Statistics, or a related technical discipline; a Master's degree in Electrical/Electronics Engineering, Statistics, Data Analytics, or a related field is preferred.
• 3–5 years of experience in semiconductor manufacturing, product engineering, quality engineering, reliability, test engineering, or a closely related industry.
• Strong understanding of statistical methodologies, including Statistical Process Control (SPC), outlier detection, distribution analysis, trend analysis, data visualization, and risk assessment.
• Practical knowledge of semiconductor manufacturing processes, wafer fabrication, probe testing, and parametric testing methodologies.
• Understanding of quality systems, risk management, defect containment, and quality disposition processes.
• Strong analytical and problem-solving abilities, with the capacity to interpret complex manufacturing datasets and translate findings into actionable recommendations.
• Experience with quality engineering, reliability engineering, or semiconductor product disposition is an advantage.
• Familiarity with memory technologies such as DRAM, NAND, HBM, or other emerging memory products is preferred.
• Exposure to machine learning, predictive analytics, or AI-enabled manufacturing applications is beneficial.
• Experience developing dashboards using Tableau, Power BI, Spotfire, or similar visualization platforms is a plus.
• Familiarity with enterprise automation, workflow systems, and digital transformation initiatives is desirable.
• Knowledge of quality methodologies such as FMEA, CAPA, 8D, Six Sigma, or Lean Manufacturing is an advantage.
• Strong written and verbal English communication skills, with the ability to present technical findings clearly.
• Demonstrated ability to collaborate effectively across global, multicultural, and cross-functional teams.
• Strong organizational skills and the ability to manage multiple priorities while maintaining accuracy and attention to detail.
• Self-motivated, accountable, and proactive, with a strong ownership mindset and commitment to continuous improvement.
• Experience supporting operational teams across multiple manufacturing sites is preferred.

Benefits:

• Opportunity to contribute to advanced semiconductor manufacturing and next-generation memory technologies.
• Exposure to global quality operations across multiple manufacturing locations and international teams.
• Hands-on experience with advanced statistical analysis, automated quality systems, data analytics, and digital transformation.
• Opportunities to work with AI, machine learning, predictive analytics, and automation applications in manufacturing.
• Cross-functional collaboration with quality, reliability, product engineering, manufacturing, and software development teams.
• Professional development opportunities in semiconductor quality engineering and operational excellence.
• Opportunity to contribute to global standardization, continuous improvement, and scalable quality processes.
• Full-time employment based in Hyderabad, India.
• Competitive compensation and benefits package, subject to applicable employment terms and policies.
• Inclusive and collaborative work environment focused on innovation, integrity, operational excellence, and continuous learning.