DATA SCIENTIST

Аналитика рынка

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Описание

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. , 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. , 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. , 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.

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