Data Scientist

Intel Intel · Semiconductors · Arizona, Phoenix, United States

Data Scientist role focused on analyzing manufacturing and process data to improve stability, reduce variation, and enhance yield in Thermal Compression Bonding (TCB) processes. The role involves developing and validating machine learning models for monitoring, prediction, and defect analysis, and collaborating with engineering teams to deploy data-driven solutions in a manufacturing environment.

What you'd actually do

  1. Analyze manufacturing, process, equipment, metrology, and yield data to identify trends, correlations, anomalies, and root causes of process variation.
  2. Develop and validate data analytics and machine learning models to support: process monitoring, excursion detection, yield prediction, defect pattern analysis and tool health / process drift monitoring
  3. Build proof-of-concepts to demonstrate the technical feasibility of predictive analytics and ML-based methods for TCB and related manufacturing applications.
  4. Work with process and equipment teams to define analytics requirements and translate manufacturing problems into scalable data solutions.
  5. Collaborate with manufacturing stakeholders to deploy practical solutions that can be used in production environments.

Skills

Required

  • Python
  • JMP
  • SQL
  • data analysis
  • statistical analysis
  • machine learning models
  • predictive modeling
  • large datasets

Nice to have

  • semiconductor manufacturing
  • advanced process control
  • yield analysis
  • root cause investigation
  • anomaly detection
  • clustering
  • classification
  • dashboards
  • reports
  • analysis tools
  • computer vision
  • automated inspection data

What the JD emphasized

  • manufacturing data analysis
  • AI/ML
  • predictive modeling

Other signals

  • Develop and validate data analytics and machine learning models
  • Build proof-of-concepts to demonstrate the technical feasibility of predictive analytics and ML-based methods
  • Apply AI/ML, statistical methods, or predictive modeling to extract actionable insights