Sputter Equipment Maintenance Intern, Data Science (Diploma Internship)

Sputtering is the heart of media process in recording media organization.  We are a dynamic team working in a world’s largest state-of-the art magnetic media manufacturing environment with highly automated processes and advanced control systems.  

We pride ourselves on delivering committed equipment performance and operational objectives through effective maintenance, technology, and innovation to help Seagate succeed.  Our team is made up of committed, talented, and friendly people passionate about our job, believing in people well-being and people development. 

As a Data Science Intern in the Sputter Equipment Maintenance team, you will work alongside experienced engineers to enhance equipment performance and operational efficiency. Collaborate with cross functional team to explore how data science techniques can be applied to enhance equipment reliability. In this role, you will: 

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  Support in analyzing equipment performance data to understand downtime trends (OEE) and assist in visualizing these trends. 

  
  

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  Support in building and maintaining data pipelines for data processing tasks. 

  
  

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  Assist in preparing and cleaning data for analysis, under the guidance of experienced team members. 

  
  

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  Work on predictive modelling algorithms or solutions to predict preventative maintenance schedules, with supervision. 

  
  

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  Contribute to coding tasks and data source management, learning how to interpret Python code and perform updates on internal tools or websites. 

  
  

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  Strong analytical skills with a passion for working with large datasets and solving real-world problems. 

  
  

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  Understanding of machine learning concepts and their applications in real-world scenarios. 

  
  

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  Familiarity with cloud platforms and version control systems (e.g., Git). 

  
  

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  Understanding of managing data pipelines, Big Data Analysis and database development. 

  
  

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  Currently pursuing a diploma in Data Science, Engineering, or a related field. 

  
  

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  Familiarity with Python, machine learning algorithms, and data visualization tools. 

  
  

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  Experience with data analysis tools such as pandas, NumPy, or scikit-learn. 

  
  

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  Knowledge of predictive maintenance concepts or experience in a manufacturing environment.