Brad Powley
Senior Member Technical Staff, Machine Learning, Salesforce
Brad Powley has extensive experience in engineering and strategy consulting. He works for Salesforce, designing and building data products that deliver insights to customers through machine learning and distributed graph processing. As a consultant, he managed engagements in energy, life sciences, real estate, IT, and manufacturing, helping his clients make better strategic decisions. He has also practiced the disciplines of mechanical design, manufacturing, and systems engineering in the context of high-volume consumer electronics products and a global supply chain.
Brad completed his PhD in Management Science and Engineering at Stanford, advised by Prof. Ron Howard. His thesis, Quantile Function Methods for Decision Analysis, set the theoretic foundation for a set of probability distributions particularly well-suited for Decision Analysis and other applications. He is particularly interested in the intersection of artificial intelligence and human decision making.
My Sessions
Bringing out the Data Scientist in the Decision Analyst
The Capital PeakBrad Powley
Isaac Faber
Dennis Buede
David Blum
Stephen Malinak
Decision quality has six elements, and only one has to do with information. So why all the fuss about data science? Does Data Science simply bring a new set of tools to the decision analyst’s toolkit, or are there more fundamental principles on offer? What should the modern decision analyst learn in order to remain […]
Bringing out the Decision Analyst in the Data Scientist
The Capital PeakBrad Powley
John Mark Agosta
Zahit Guneri
Aaron Schaffrina
John Busbice
Data Science is a nascent and increasingly popular interdisciplinary field, wherein the data scientist uses large datasets to derive insights and inform decisions. Despite this, Decision Analysis is rarely mentioned as a necessary skill for a data scientist. What are the decision making gaps in Data Science, and what can — and should — decision […]