Product Engineer, Data Analytics

Key Responsibilities

• 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.
• 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.
• 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.

Requirements

• Bachelor's or Master's degree in Engineering, Computer Science, Statistics, Artificial Intelligence, or related fields.
• Demonstrate strong technical skills with a deep understanding of data analytics, and analysis tools such as JMP, Tableau, and Power BI.
• Proficiency in programming languages such as Python, R, SQL, and web application languages like JavaScript, HTML, and CSS for data manipulation and analysis.
• Proficient in statistical analysis, predictive modeling, and machine learning techniques, including libraries such as TensorFlow, Scikit-learn, and PyTorch.
• Excellent communication skills to present findings and insights to non-technical stakeholders.
• Strong sense of responsibility and accountability towards assigned role with professional work ethic.

Required to work onsite in Singapore with international travel to Taiwan.