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Cincinnati, OH

Internship

Hybrid

$60.57/hr - $60.57/hr

Intern

Procter & Gamble is a global consumer packaged goods company with a Research and Development organization focused on product innovation and scientific analysis. The PhD Intern will develop generative AI tools, investigate active learning and sequential experimentation, evaluate consumer study and simulation datasets, and optimize experimental resources.

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Responsibilities

Using historical consumer study and simulation datasets to evaluate uncertainty
Identify next study, simulation, and measurement based on reduction of uncertainty
Optimize experimental resources
Create a prototype work process to demonstrate success

Qualification

Required

**Education:** Working towards a PhD in Computer Science, Machine Learning, Statistics, Mechanical Engineering, Computational Physics, Computational Engineering, Applied Mathematics, or Data Science
Active learning / sequential experimental design — central to recommending the next most informative consumer study or simulation
Probabilistic machine learning / Bayesian statistics — needed to represent prediction uncertainty and update confidence as evidence arrives
Surrogate modeling — needed for both consumer-response prediction and simulation-response approximation
Python — required for model development, simulation, and analysis; PyTorch, scikit-learn, BoTorch, GPyTorch, or similar tools would be useful
Experimental design and model evaluation — needed to compare active learning policies against baselines and define success metrics
Available to work a 12-week internship from May/early June to August in the summer of 2027, and at least 3 days/week onsite at Winton Hill Business Center
Has strong ownership & self-leadership skills to understand the business objective and technical problem to be solved
Has experience collaborating with multi-functional teams
Has the ability to recognize opportunities for capabilities or technologies that can be reapplied across programs
Can independently work project priorities, multi-task, and handle a wide variety of complex, non-routine/routine tasks in a fast-paced environment

Preferred

Experience with entrepreneurial/startups and/or industrial experience are strongly preferred
Bayesian optimization
Multi-armed bandits or reinforcement learning
Uncertainty quantification
Gaussian processes, evidential models, Bayesian neural networks, or ensembles
Scientific computing
CFD / FEA familiarity, if using first-principles simulation examples
Generative AI / AI-agent prototyping, but only as a communication layer, not the core research contribution