Data Science Leadership - The Role Of Effective Process For Successful Outcomes

Amir Emadzadeh, Director, Software engineering, Genentech (Roche)
Rick Ballmann, Vice President Of Engineering, Data Intelligence and Customer Experience, American Express
Pranay Chandur, Associate Director Data Science, Walmart
Benjamin Harvey, CEO, AI Squared
Abstract:
Building machine learning applications might be technology heavy, but ultimately the decision makers and most important actors are human. To get your average model into production, many disparate teams must collaborate effectively to bring about a widely supported objective. This objective typically requires the approval of several different executive leaders, including the eventual end users of whatever the model is supposed to do. If the objective is poorly understood, if proper planning is not carried out, or if the objective is not met with broad support, failure is probable. Therefore, technical leadership within data science, engineering, and IT groups must be exceedingly thoughtful and tactical in how they approach the development of machine learning models at scale. This panel will gather technical leadership to discuss the role of process in doing AI well.