Data analytics engineer on a semiconductor (HBM) manufacturing team, owning yield and quality improvement: developing ML/deep learning models for wafer/die screening, analyzing engineering data with statistical tools, and building cloud data pipelines with fab, design, and reliability teams. Core stack: Python, R, SQL, JMP, Tableau, TensorFlow/PyTorch.
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.