Pharma Manufacturing Co-Op: Process Improvement & Data

Bayer CropScience Limited; Myerstown (PA); On-site; USD 33,000 - 59,000; Full time

Job summary

Bayer CropScience Limited invites applications for a Manufacturing Co-Op in Myerstown, PA. Candidates are pursuing Engineering or Data Science and will engage in projects spanning changeover reduction, 5S, data analysis, and kaizen events.

Training includes pharmaceutical manufacturing basics and working with cross-functional teams. Strong English communication is required, along with comfort analyzing data and solving problems under tight timelines.

Qualifications

• Pursuing a Bachelor or Master’s in Engineering, Data Science, or related field.
• Must have completed at least 1 year of degree program.
• Strong verbal and written English communication skills.
• Experience with data analysis and root cause analysis.

Responsibilities

• Assist in projects including changeover reduction, 5S, Pareto analysis, time study, value stream mapping, standardized work, and kaizen events.
• Basic use of project management skills.
• Troubleshoot maintenance and reliability issues.
• Learn basics of pharmaceutical manufacturing and packaging processing and equipment.
• Review equipment specifications and analyze data to support decisions.
• Collaborate with plant personnel and vendors to implement solutions.

Skills

Data analysis

Root cause analysis

English communication

Multitasking

Education

Bachelor's or Master's in Engineering or Data Science

At least 1 year completed in degree program

Tools

Microsoft Office

Excel Pivot Tables

Job description

Bayer CropScience Limited invites applications for a Manufacturing Co-Op in Myerstown, PA. Candidates are pursuing Engineering or Data Science and will engage in projects spanning changeover reduction, 5S, data analysis, and kaizen events.

Training includes pharmaceutical manufacturing basics and working with cross-functional teams. Strong English communication is required, along with comfort analyzing data and solving problems under tight timelines.