R&D PhD Summer Intern: Life Cycle Assessment (LCA) Data Science

Job Location

MASON BUS AND INNOVATION CTR

Job Description

Research and Development (R&D) at Procter & Gamble, the largest consumer packaged goods company in the world, includes a diverse group of roles that contribute to the innovation and development of our products. It encompasses roles in product research, formulation, testing, and scientific analysis. You will find variety and excitement starting Day 1. This internship is specifically designed for individuals working towards a PhD who are developing proficiency in their field. As an intern or co-op in management, you will have the opportunity to learn from experienced professionals in a supportive environment. This is a 12-week paid internship, designed to provide you with a solid foundation for future career growth. The internship will take place from May (potentially early June) to August of 2027. Join us at P&G, where your contributions will play a vital role in shaping the future of consumer products!

The Opportunity

Procter & Gamble Beauty Care Global Product Stewardship is looking for a PhD student Intern to help build the data foundation needed to scale life cycle assessment (LCA) across Beauty Care. You will develop and organize life cycle inventory data, turning information from different sources and formats into consistent, model-ready datasets to enable Beauty Care business to generate sustainability insights more efficiently and use life cycle thinking in product and portfolio decisions, supporting P&G climate goals.

The Ideal Candidate Has

Strong analytical and problem-solving skills, with attention to detail and a practical approach to complex technical data. Interest in and comfort with working with incomplete or ambiguous information and turning it into clear, consistent data structures. Strong communication and collaboration skills, including the ability to explain technical work to different audiences. Good organizational skills and a disciplined approach to documenting assumptions, methods,