About The Position

White Cap is hiring a Corporate Intern for Operations in its Customer Delivery Hub. The internship provides hands-on experience in operations and related business areas. The intern analyzes information, develops findings and recommendations, supports functional projects and process improvements, and may assist with research or special projects that advance business initiatives and objectives. The work follows established procedures and receives general supervision; the intern may be paired with a mentor and may provide general guidance or training to junior support personnel.

Requirements

  • High school diploma or GED is strongly preferred.
  • Strong analytical and problem-solving skills, with an interest in distribution, logistics, supply chain, or operational improvement.
  • Proficiency with Microsoft Excel.
  • Ability to translate data and analysis into clear insights and recommendations.
  • Strong organizational and project management skills, including the ability to manage multiple priorities.
  • Strong communication skills, intellectual curiosity, and comfort working in a fast-paced, evolving environment.
  • Ability to work under general supervision, exercise judgment, and follow established procedures or protocols.

Nice To Haves

  • Currently pursuing a bachelor’s degree in Supply Chain, Operations, Industrial Engineering, Business, Finance, Data Analytics, or a related field.
  • Experience with Power BI or other data visualization tools.
  • May be paired with a mentor.

Responsibilities

  • Prepare and deliver insights and recommendations based on analyses.
  • Produce findings and conclusions from analyses and make oral and written recommendations to management.
  • Execute tasks related to functional projects and process improvements.
  • Communicate issues and roadblocks related to areas of responsibility.
  • Assist with research or special projects to fulfill business initiatives and meet business objectives.
  • Resolve missing or incomplete information and inconsistencies or anomalies in routine research and data.
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