The Advanced Packaging Technology and Manufacturing (APTM) organization is looking for data scientist to support Thermal Compression Bonding (TCB) process control, manufacturing data analytics, yield improvement, and predictive modeling. The successful candidate will work closely with process engineers, equipment engineers, metrology, quality, and manufacturing teams to develop data-driven solutions that improve process stability, reduce variation, and enhance yield.

Key Responsibilities

• Analyze manufacturing, process, equipment, metrology, and yield data to identify trends, correlations, anomalies, and root causes of process variation.

• 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

• Build proof-of-concepts to demonstrate the technical feasibility of predictive analytics and ML-based methods for TCB and related manufacturing applications.

• Work with process and equipment teams to define analytics requirements and translate manufacturing problems into scalable data solutions.

• Support advanced process control initiatives

• Collaborate with manufacturing stakeholders to deploy practical solutions that can be used in production environments.

• Develop dashboards, reports, and automated analysis tools to improve decision-making and reaction time.

• Maintain a strong focus on data quality, model validity, and manufacturing relevance.

Behavioral traits that we are looking for:
• Strong communication and collaboration skills with the ability to work across process, equipment, metrology, quality, and manufacturing teams.
• Proactive and self-driven, with the ability to work independently and manage multiple priorities.
• Comfortable working in a dynamic manufacturing environment with changing priorities and imperfect data.
• Able to translate complex analytical results into clear actions for non-data experts.

Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.

Minimum Qualifications
• Bachelor's degree with 6+ years of relevant experience, or aMaster's degree with 4+ years of relevant experience,or a PhD with 2+ years of relevant experience in Computer Science, Data Science, Statistics, Electrical Engineering, Industrial Engineering, Mechanical Engineering, or another relevant science or engineering discipline.
• Experience in manufacturing data analysis, statistical analysis, and applying data-driven methods to solve process or yield problems.
• Strong proficiency in Python programming and JMP for data analysis, automation, and model development.
• Experience working with large datasets from manufacturing, equipment, metrology, or quality systems.
• Demonstrated ability to apply AI/ML, statistical methods, or predictive modeling to extract actionable insights and support business or engineering decisions.
• Knowledge of SQL or other database query tools for data extraction and analysis.

Preferred Qualifications
• Experience in semiconductor manufacturing, preferably assembly / advanced packaging.
• Understanding of advanced process control (APC), including run-to-run control, SPC, excursion detection, and process monitoring.
• Experience with yield analysis, process capability improvement, and root cause investigation in a manufacturing environment.
• Familiarity with machine learning models for anomaly detection, prediction, clustering, or classification.
• Experience developing dashboards, reports, or analysis tools.
• Experience working with computer vision or automated inspection data is a plus.

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel.

Work Model for this Role

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