Associate Professor | Program Director, MS in Applied Data Science
233 Hinds Hall
My research focuses on the socio-technical issues relating to data science, such as data science project management, and how teams should work together to “do” data science.
My 20+ years of industry experience has often focused on building new teams that leverage emerging technologies and data analytics to deliver innovative business solutions. In my last corporate role, I worked at JPMorgan Chase. At JPMC, I reported to the firm’s Chief Information officer while helping to drive innovation throughout the firm. I also held several other key management positions at the company, including CTO (Consumer and Community Banking Risk Management), Chief Information Architect (Chase Financial Services), global head of eBusiness technology and vice president of computational technology. I previously served as chief technology officer and principal investor at Goldman Sachs/Goldman Sachs Ventures, where I invested and helped incubate technology start-ups. I started my career as a programmer, project leader and consulting engineer with Digital Equipment Corp (now part of HP).
I received my B.S. in computer science from Cornell University, an M.B.A. from The Wharton School at the University of Pennsylvania and a Ph.D. in Information Systems from the New Jersey Institute of Technology.
Big Data Science: Frameworks to improve the process teams use while performing data projects
My key research question is “would having a well defined methodology improve the results of teams doing big data projects”. While data science projects are growing in frequency and importance, the growth in the use of data science has outstripped the knowledge of how to structure projects and project teams to ensure that they perform reliably and effectively. This is similar to the early days of software development: software was being developed but organizations had little ability to predict whether a project would be successful, on time or on budget and projects were overly reliant on the heroic efforts of particular individuals.
For general information on data science project management, check out: www.datascience-pm.com
For information on data science process training and certification, take a look at: www.datascienceprocess.com
To explore a new continuous flow framework for agile data science projects, that integrates the structure of Scrum and the continuous flow of Kanban, go to: www.datadrivenscrum.com