YOUR FUTURE EMPLOYER
From its foundation 185 years ago as a soap and candle start-up, P&G today is a leading consumer goods company. We are home to iconic, trusted brands that touch 5 billion consumers worldwide and make life a little bit easier in small but meaningful ways.
Our people are our greatest asset: with our philosophy of promotion from within, we place strong emphasis on employee development and are committed to finding and fostering world-class talent. Learn from our inspiring leaders, shape our supportive and welcoming culture, and place your personal development at the core of your work
Are you eager to lead exciting projects and have a meaningful impact with your ideas? At P&G, our Engineering team delivers the technologies that make product innovation possible. Together, we create irresistible, meaningful, and sustainable experiences for our consumers. Every change we make in our production systems meet rigorous qualification standards with solid foundation in statistics, ensuring the quality of our products.
From Day 1, you will be exploring & developing alternative, more affordable sampling techniques & tools with equivalent statistical confidence by
* Evaluating the foundations of our qualification protocols (i.e. Binomial SPRT) & comparing vs. the state-of-the-art.
* Analyzing historical data from qualification events & study opportunities to leverage prior experiences, e.g. accounting for factors or events influencing total probabilities.
* Performing proper simulations in various scenarios to estimate rate of attribute defects.
* Initiate a database that enables historical trend analyses.
* Evaluate possible applications of ML approaches (e.g. Genetic Algorithms)
* Make progress in the subject and Integrate the learning experience to make a compelling case for the organization to implement any applicable improvement.
We believe expertise or interest in developing in one or more of the following techniques may be relevant: Bayesian optimization, Network meta-analysis, Gaussian Process Modeling, and Mixed effect modeling, as well machine learning modeling techniques.