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Bloomington, MN

Internship

Remote

$24/hr - $35/hr

Intern

Seagate Technology is a leader in data storage solutions, and they are seeking a Business & Data Analytics Intern for Summer 2026. The role involves conducting business and operational analysis, building analytical models, and collaborating with engineering teams to optimize processes.

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Responsibilities

Conduct business and operational analysis to quantify how Virtual Metrology (VM) models affects cycle time, tool availability, throughput, and overall factory efficiency.
Build analytical models to estimate cost savings, risk reduction, and ROI generated by VM at scale.
Evaluate metrology workflows to identify where VM can reduce bottlenecks or improve decision speed.
Collaborate with engineering teams to map current vs. VM-enabled processes, highlighting opportunities for optimization and automation.
Translate technical outputs (prediction accuracy, uncertainty, sampling strategies) into business-relevant insights for leadership.
Prepare reports and presentations that clearly communicate findings, scenarios, and recommendations.

Qualification

Required

Effective communication skills, with the ability to translate technical information into clear business recommendations.
Curiosity, initiative, and comfort working across engineering, operations, and business teams.
Demonstrated interest in manufacturing systems, automation, and data-driven business strategy.
Strong analytical and problem-solving skills, with experience in business analysis, operations research, or data analytics.
Familiarity with semiconductor or high-volume manufacturing, including process flows and metrology concepts.
Ability to analyze both technical performance metrics and their business implications.
Proficiency in tools such as Python, SQL, or statistical analysis packages.
Currently pursuing a Bachelor’s or Master’s in Industrial Engineering, Data Science, Operations Research, Business Analytics, or a related field with enrollment in classes in Fall 2026.

Preferred

Experience with predictive modeling or manufacturing analytics (e.g., virtual metrology, anomaly detection).
Understanding of cost modeling, ROI estimation, or operational efficiency analysis.
Background in industrial engineering, factory optimization, or supply chain analytics.
Familiarity with KPI development and dashboard creation for technical/business metrics.