Product Engineer (Advance Data Analytics)

EIRE Systems; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

EIRE Systems in Singapore is seeking a Product Engineer (Advanced Data Analytics) to lead data-driven yield and quality initiatives across cross-functional teams. You will apply ML/AI techniques to engineer data analysis and improve fabrication outcomes.

You will develop models for wafer/die level screening, build data pipelines with cloud tools, and collaborate with Fab, HBM Technology Development, and Quality/Reliability teams to ship robust products.

Qualifications

• Degree in Engineering, CS, Stats, AI, or related field.
• Strong analytics background and data tools expertise.
• Proficiency in Python, R, SQL.
• Experience with ML/DL libraries (TensorFlow, PyTorch).
• Excellent communication with non-technical stakeholders.
• Onsite work in Singapore with travel to Taiwan.

Responsibilities

• Lead yield and quality initiatives across cross-functional teams.
• Perform data analysis using ML/AI to improve yields and reliability.
• Develop ML/DL models for quality, yields, and screening.
• Develop data infrastructure with cloud technologies to support assessments.
• Collaborate with Fab, HBM teams to ship products.
• Promote innovation to maintain competitive edge.
• Advocate AI/ML to improve KPIs (Quality, Cost, Cycle Time, Scale).

Skills

Data analytics

Python

R

SQL

Machine Learning

Communication

Data visualization

Tableau

Education

Bachelor's degree in Engineering

Master's degree in CS/AI

Tools

JMP

Tableau

Power BI

TensorFlow

Scikit-learn

PyTorch

Job description

Product Engineer (Advance 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.