Managing Data Science Projects via an Agile Framework

Abstract: 

Data science managers (and senior leaders managing data science teams) need to think through many questions relating to how to best execute their data science efforts. For example, how should the team brainstorm ideas, how should the team prioritize those potential ideas, and more generally, how to help ensure the team delivers actionable insights. While these management challenges are very different than technical machine learning challenges that most teams focus on trying to solve, the management challenges are equally important to address to ensure a successful data science project. In other words, the focus of this talk is not on which specific algorithm a team should use, but rather, how to ensure that the data science effort is progressing effectively and efficiently.

Bio: 

Since joining Syracuse University in 2014, Jeff has focused on how to effectively manage and coordinate data science projects within and across teams. Via his research and consulting, Jeff has become an expert in this field, having worked across a range of organizations and published 30+ peer-reviewed academic papers that (1) explore the challenges in executing data science projects, and (2) evaluate potential frameworks via experiments and real-world case studies.

Prior to joining Syracuse University and consulting on data science efforts, Jeff worked at JPMorgan Chase, where he reported to the firm's Global Chief Information Officer and drove technology innovation across the organization, while also having other leadership roles within the bank, such as leading the IT effort for credit risk analytics within the consumer bank.

Open Data Science

 

 

 

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