Data Scientist

Intel•Chandler, AZ

8d•Onsite

About The Position

The Advanced Packaging Technology and Manufacturing (APTM) organization is looking for a 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.

Requirements

Bachelor's degree with 6+ years of relevant experience, or a Master'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.

Nice To Haves

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.

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.