Intern - COE Smart Manufacturing

Arconic is currently in search of a COE Smart Manufacturing Intern to join our Kawneer Business in Springdale, AR.

 

At Arconic, we are looking for people who share our values of integrity, inclusion, and diversity, and who demonstrate agility, results commitment, and the capability to grow themselves and others. In return, we offer the opportunity to Grow Together with ongoing opportunities for professional growth provided by a constantly changing environment, working alongside employees who value the people they work with just as much as the work they do.  

Basic Qualifications:

• Must be currently enrolled in a Bachelor’s degree program in Industry 4.0, Smart Manufacturing, Data Science, Marketing, or Engineering
• GPA of 3.0 or above
• Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at time of hire. Visa sponsorship is not available for this position

Preferred Qualifications:

• Prior work experience​​
• Involved in at least 1 extracurricular activity.

 

Under the direction of the Smart Manufacturing COE Manager, this position is accountable for the development of data analytic strategies and systems capable of increasing the efficiency and problem solving capabilities within BCS.

• Organize, construct, and deploy systems capable of analyzing and displaying large amounts of data in formats that meet the organizational requirements.
• Engage the organization deeper into problem solving efforts utilizing performance metrics, cost components, quality data, design parameters, machine parameters, process characteristics, etc., using various tools including but not limited to:
  • Business Intelligence Suites
  • Power BI
  • Databases (SQL and OSI PI)
  • Reporting Tools
  • Statistical Analytical Software
• Perform “what if” analysis and communicate recommendations to both operational and IT leaders. Collaborate with Process Engineers and IT resources to utilize hidden potential in the manufacturing systems.
• Identify and correct data limitations (e.g. signal / noise ratio, sample size, inconsistencies, missing values, corruption, etc.).
• Design and implement dashboards and key metric scorecards.
• Determine potential causes of problems and devise data testing methodologies for validation.
• Search and probe questions with peers and process experts that ultimately leads to action and recommendations.
• Guide and direct small teams to achieve project objectives including the set up and execution of smart manufacturing deployment across BCS locations. 
• Streamline and improve existing data analytics tools within the continuous improvement processes and systems.
• Use data, best practices and Kaizen methodologies to increase process efficiency and reduce operational cost.