DATA SCIENTIST

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.