Building and Managing World Class Data Science Teams
Building and Managing World Class Data Science Teams


Many enterprises are trying their hands at data science and failing miserably. One of the key factors this is happening is a severe shortage of qualified data science leadership. The aim is for attendees to walk away with a practical framework to use to identify where they have gaps as enterprises and as individuals and identify potential solutions.

The framework begins with the key pre-requisites a company must put in place to lay the proper groundwork for data science initiatives. It then moves through people, process, and technology considerations, with specific recommendations as to how to address these dimensions successfully.

Finally, there is a focus on the key ingredient missing from most organizations, good data science leadership. We will discuss why this is so critical, elements to put in place that will help data science leaders become more effective, and lay out potential design considerations for creating development paths that support both the growth of technical individuals, but also those interested in managing, and some of the key points which should be differentiated.


Conor Jensen is an experienced Data Science executive with over 15 years working in the analytics space. He is the founder of Renegade Science, a Data Science strategy and coaching consultancy and works as a Customer Success Team Lead at Dataiku, helping customers make the most of their Data Science platform and guiding them through building teams and processes to be successful. He took his lumps building out data science teams and platforms at multiple large enterprises and does his best to help other companies not repeat his mistakes.