The best data is the right data, even if it's small.
The key to successful decision making is responsible computation with the right data. The methodologies I use are backed by mathematical, statistical, and economic theory. Unlike many of the black box methods in AI and machine learning, they are transparent and interpretable. My expertise is in Bayesian econometrics with a focus on discrete choice modeling including conjoint and maxdiff analysis. In addition, I use adaptive experimental designs, A/B testing, recommendation models, and matching algorithms to generate the insight and confidence needed to make good business decisions. Here are some examples:
I am happy to teach workshops on choice modeling, conjoint analysis, Bayesian statistics, causal inference, or other topics related to data science and business. Contact me if you'd like me to teach a workshop.