DATA SCIENTIST
Summary
Data scientist optimizing semiconductor manufacturing yield by analyzing sensor and tool data, building ML models, and automating dashboards for engineering teams.
Key Responsibilities
Yield & Process Optimization: Collaborate with semiconductor manufacturing engineering teams to analyze inline/param/probe data to identify top yield detractors and drive continuous improvement.
Data Pipeline & Automation: Extract, cleanse, and analyze datasets from SQL databases, sensor networks, and fabrication tool logs to support semiconductor manufacturing operations.
Advanced Analytics & Modeling: Apply data science techniques, statistical modeling, and machine learning to solve yield issues and support defect reduction strategies.
Experimentation Support: Assist process and integration engineers in running and analyzing Design of Experiments (DOE) to enhance process capabilities and margins.
Visualization & Communication: Develop automated reports and dashboards using visualization tools (e.g., Dash, Plotly, Angular) to communicate technical concepts and project outcomes effectively to engineering stakeholders.
Required Qualifications
Bachelor's degree in Computer Science, Data Science, Statistics, AI, or a related Engineering field.
Hands-on experience in data science, analytics, or scripting applications.
Willingness to learn semiconductor manufacturing principles and collaborate closely with equipment and integration engineers to resolve production issues.
Required Technical Experience
Programming & Data Engineering: Strong Python programming skills and working experience with SQL for data extraction and manipulation.
Statistical Analysis: Familiarity with statistical tools, methodologies (such as SPC, DOE, or FDC/EDA), and data-driven problem solving.
Data Visualization: experience applying data visualization tools (e.g., Dash, Plotly, Angular) to present complex engineering data clearly.
Data Science/AI Fundamental Knowledge: Familiarity with mathematical theory behind machine learning models, neural networks, LLM, etc.
Preferred Experience
Prior experience or internship in the semiconductor industry, electronics manufacturing, or related fields.
Basic understanding of semiconductor fabrication processes, equipment, and device physics (e.g., CMOS basic knowledge).
Familiarity with advanced analytics or computer-based analysis for manufacturing and yield applications.
Knowledge of memory architecture (DRAM/NAND).
Required Soft Skills
Effective communicator and collaborator, capable of bridging the gap between data science and traditional semiconductor engineering teams.
Analytical and problem-solving mentality with a demonstrated commitment to quality and continuous improvement in a fast-paced environment.
Proven ability to work independently, manage multiple priorities, and deliver high-quality results.