Research and Development (R&D) at Procter & Gamble 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.
Job Description:
P&G has an opportunity for a Data Science PhD intern to work with Human Safety Toxicologists in our Fabric and Home Care business to develop digital capabilities leveraging large language models (LLMs). This is an entry-level role for employees who are developing proficiency in their field. As an intern or co-op in management, you will have the opportunity to grow and learn from experienced professionals in a supportive environment.
The ideal candidate will demonstrate a strong eagerness to learn and grow professionally and possess excellent communication skills—both written and verbal. This role is perfect for those with a forward-thinking mindset who thrive on overcoming challenges. Join us in this dynamic environment, where your contributions will make a real impact as part of a collaborative team!
Key Responsibilities:
- Partner seamlessly with our Fabric & Home Care Human Safety organization to work on projects leveraging LLMs.
- Develop an agentic AI system to accelerate chemical hazard property predictions and document generation for internal and external usage.
- Build benchmarks, evaluation datasets, metrics, and methods to assess and improve the performance and effectiveness of LLM and prompts and drive iterative enhancements.
- Analyze latest LLM innovations and explore opportunities to apply cutting-edge techniques for building high-impact solutions that enhance internal capabilities and deliver exceptional user experiences.
Job Qualifications
Required Qualifications:
- Education: Working towards a PhD in Computer Science, Computer Engineering, Engineering or Life Sciences (Toxicology, Pharmacology, Biochemistry, etc.).
- Technical Skills: Proficient in programming languages such as C, C++, Python.
Preferred Qualifications:
- Analytic Methodologies: Experience with applying Machine Learning, Data Science, Generative AI and Large Language Modeling (LLM) to real-world problems.
- DevOps Familiarity: Familiarity with DevOps environments, including tools like GitHub.
