Data Scientist

Intel•Rio Rancho, NM

21d•Hybrid

About The Position

The Advanced Packaging Technology and Manufacturing (APTM) Yield Systems Organization is seeking a curious, thoughtful, and highly motivated Data Scientist with strong analytical capabilities and excellent communication skills. In this role, you will collaborate with yield analysts, engineers, and business partners to identify manufacturing yield challenges and translate them into scalable, data-driven system solutions that improve operational efficiency and product quality. The ideal candidate combines expertise in data science, automation, AI/ML, computer vision, and software development with a passion for solving complex manufacturing problems. You will play a key role in developing dynamic solutions that enhance yield performance, reduce manual effort, and strengthen APTM's overall technical capabilities.

Requirements

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 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).

Nice To Haves

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.
Execution of data science POCs into scalable and deployable solutions
Working knowledge of GAJT and SQL Pathfinder, analytics packages like R, JMP
Working knowledge of Git workflows, CI/CD pipelines, containerization, and Kubernetes for infrastructure and deployment management.
Experience with segmenting and troubleshooting day to day issues utilizing any relevant logs to identify and providing recommendations/implement fixes
Working experience building queries around the different Intel databases with a clear understanding of the location of data across these databases
Experience utilizing Klarity/ICE
A general understanding of fab and APTM process flow and tool functionality

Responsibilities

Partner with yield analysts, engineers, and business stakeholders to understand manufacturing yield issues and convert business needs into clear system requirements.
Design, develop, and deploy 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.