Your responsibilities will include developing cutting-edge algorithms, creating robust product disposition solutions, and establishing a proactive deviation alert system for yield and quality, enhancing data infrastructure, mentor team members, and fostering innovation to maintain a competitive edge. Cumulative Yield Ownership: Be a key member of cross-functional teams, to lead impactful initiatives that significantly enhance overall yield and quality improvement, driving substantial benefits for the organization. Data Analysis for Yield Improvement and Reliability: Utilize in-house statistical tools, machine learning, and AI for engineering data analysis to enhance yields and reliability, integrating these improvements within the Dispo workflow. Models/Algorithm Development: Develop state-of-the-art algorithms, including Machine Learning and Deep Learning models, to advance data mining and pattern recognition for quality enhancement, yield improvement, wafer/die level screening and efficiency enhancement. Product Disposition Solutions: Develop, validate and deploy product-disposition solutions (wormhole) to ensure quality and reduce Defects Per Million (DPM) through a comprehensive product disposition program/workflow. Deviation Alert System: Create a deviation alert system to monitor yield loss, characteristic/parameter trends, and wafer/die/cube shading. Data Infrastructure Development: Collaborate with IT, Manufacturing, and Operations teams to develop and enhance the data infrastructure pipeline using cloud technologies to support reliability and engineering assessments. Mentorship and Development: Actively mentor and develop team members to foster growth and development within the team and the organization. Cross-Functional Collaboration: Work closely with various cross-functional teams, including Fab, HBM Technology Development, HBM Design, System Development, and Quality/Reliability teams, to ensure the holistic development and successful shipping of end products. Promotion of Innovation: Promote innovation and drive changes that provide a technical advantage over competitors, maintaining the company's competitive edge in the market. AI/ML Advocate: Collaborate with cross-functional teams to develop, deploy, and validate AI/ML models aimed at enhancing key performance indicators (KPIs) such as Quality, Cost, Cycle Time, and Scale. Bachelor's or Master's degree in Engineering, Computer Science, Statistics, Artificial Intelligence, or related fields. At least 6 years of experience in data analytics/science industry. Proven experience in leading analytic projects from conception to completion. Demonstrate strong leadership and technical skills with a deep understanding of data analytics, data extraction, and analysis tools such as JMP, Tableau, and Power BI. Highly proficient in statistical analysis, predictive modeling, and machine learning techniques, including extensive experience with libraries such as TensorFlow, Scikit-learn, and PyTorch. Proficiency in programming languages such as Python, R, SQL, and web application languages like JavaScript, HTML, and CSS for advanced data manipulation and analysis. Excellent communication skills to present findings and insights to non-technical stakeholders. Proven track record of collaborative work within/across teams to address sophisticated business and engineering problems. Experience in mentoring and coaching team members to develop their skills. Dedicated and highly motivated with a flexible approach towards adapting to different roles in a dynamic working environment (from leading the team to leading technical programs). Strong sense of responsibility and accountability towards assigned role with professional work ethic. Work on site in Singapore with international travel to Taiwan.
