Back to Square One: Building a Data Science Team from Scratch

Data Science
Location: Room 1-6 Level: Non-technical
Average rating: ****.
(4.00, 12 ratings)

Generally speaking, big data and data science originated in the west and are coming to Europe with a bit of a delay. There is at least one exception though: The London-based music discovery website is a data company at heart and has been doing large-scale data processing and analysis for years. It started using Hadoop in early 2006, for instance, making it one of the earliest adopters worldwide. When I left to join Massive Media, the social media company behind and, I basically moved from a data science forerunner to a newcomer. Massive Media had at least as much data to play with and tremendous potential, but they were not doing much with it yet. The data science team had to be build from the ground up and every step had to be argued for and justified along the way. Having done this exercise of evaluating everything I learned at and starting over completely with a clean slate at Massive Media, I developed a pretty clear perspective on how to find good data scientists, what they should be doing, what tools they should be using, and how to organize them to work together efficiently as team, which is precisely what I would like to share in this talk.

Photo of Klaas Bosteels

Klaas Bosteels

Massive Media

Klaas Bosteels is the Lead Data Scientist at Massive Media, the social media company behind and Before joining Massive Media, he worked on various big data problems as a member of the music information retrieval team at He has contributed code to several open source projects related to data processing and analysis, the largest one being Apache Hadoop, and is the creator of Dumbo, a Python API for writing and running MapReduce applications. He was also the main organizer of a series of Hadoop User Group UK meetups and is a founding member of Klaas has a Ph.D. in Computer Science from Ghent University.


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