Data Scientist

Key Responsibilities

• Partner with yield analysts, engineers, and business stakeholders to understand manufacturing yield issues and convert business needs into clear system requirements.
• Create scalable data science solutions that address yield-related challenges across advanced packaging manufacturing operations.
• Create automated workflows and data pipelines that improve efficiency, accuracy, and decision-making.
• Apply machine learning, artificial intelligence, statistical analysis, and computer vision techniques to identify patterns, detect anomalies, and drive yield improvements.
• Develop and maintain software tools, dashboards, and applications that provide actionable insights to manufacturing teams.
• Leverage manufacturing and fabrication (fab) data to generate dynamic, real-time solutions that enhance operational performance.
• Collaborate across multidisciplinary teams to implement and support production-ready systems.
• Communicate technical findings, recommendations, and project outcomes effectively to both technical and non-technical audiences.
• Continuously evaluate emerging technologies and methodologies to improve APTM's yield analysis and manufacturing capabilities.

Qualifications

• Bachelor's degree with 3+ years of relevant experience or master's degree with 2+ years of relevant experience in Computer Science, Data Science or any other Engineering discipline.
• Experience mentioned above should be in the following areas:
• Experience working with advanced packaging data as well as Fab data (defects, yield, FDC, etc.) for more than 1 year.
• Python programming for frontend and backend system pipeline development including data parsing, ETL pipelines, UI development framework for data visualization, database integration with both relational (e.g. MySQL, PostgreSQL) and NoSQL instances (e.g. MongoDB), and API development.
• Git for version control and CI/CD pipelines for automated deployment.
• 1 year or more experience with defect data workflows and automation systems (Fab Tools, Station Controllers and Adaptive Metrology).
• 1 year or more of Layer owner experience, DefMet tool exposure or Fab experience (tool ownership, yield/integration, etc.).
• 1 year or more experience with UDB, YAS data structures and workflows.
• Experience building and troubleshooting ETL flows using Intel databases, especially with unstructured or missing advanced packaging/manufacturing data.
• Experience supporting production data flows, dashboards, or tools used by engineering teams.
• Experience working with cross-functional teams including process, yield, integration, metrology, automation, or data teams.
• Experience managing multiple projects simultaneously against varying priorities within the team.
• Hands-on experience with end-to-end data engineering workflow from data ingestion, cleaning, analytics, modeling, evaluation, and deployment.
• Experience with segmenting and troubleshooting day to day issues utilizing any relevant logs to identify and providing recommendations/implement fixes.
• Experience utilizing Klarity/ICEA general understanding of fab and APTM process flow and tool functionality.

Requirements

• Minimum qualifications are required to be initially considered for this position.
• This position is not eligible for Intel immigration sponsorship.

Pay

Annual Salary Range for jobs which could be performed in the US: $136,900.00 - 193,270.00 USD

Schedule

Shift 1 (United States of America)

Location

Primary Location: US, New Mexico, Albuquerque

Additional Locations

• US, Arizona, Phoenix