Data Scientist

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.
• 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.

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Company Intel

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