Job Description

About Carrier

Carrier Global Corporation, global leader in intelligent climate and energy solutions, is committed to creating solutions that matter for people and our planet for generations to come. From the beginning, we've led in inventing new technologies and entirely new industries. Today, we continue to lead because we have a world-class, diverse workforce that puts the customer at the center of everything we do.

About this role

The candidate will be responsible for participating in product cost teardowns, with a focus on mechanical components such as sheetmetal parts, plastic parts (molded and thermoform), metal weldments, cardboard packaging, etc. The candidate will be responsible for documenting and implementing one major saving idea during their internship. Working the idea from identification, scoping, prototyping, testing and implementation. The candidate will also assist team members in various projects and tasks as needed throughout the internship. All with a focus on delivering savings to the corporation.

Key Responsibilities

• Collaborate with cross-functional teams, including manufacturing, other engineering teams, procurement, production, finance, to gather relevant savings insights.
• Participate in cost teardowns and benchmarking of competitor products.
• Assist with documenting the results of product teardowns.
• Develop basic cost benefit analysis of cost reduction ideas.
• Work beside members of the team developing new mechanical designs. Learning as you go.
• With the guidance of senior team members, investigate industry trends, best practices, and emerging technologies related to product design.

Required Qualifications

• Pursuing Bachelor’s degree in Mechanical Engineering
• 3.0 or higher GPA
• Must be eligible to work in the US without sponsorship.

Preferred Qualifications

• Skills in MS Office tools (Word/Excel/PowerPoint)
• Knowledge of Mechanical design packages (example Creo/Solidworks)
• Advanced Excel skills in managing large spreadsheets and developing basic conclusions from complex data.

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