The Third Wave Data Scientist

Introduction

Drew Conway’s visualization of the data science skill set is an often cited classic. Different opinions and the versatility of the role have spawned numerous variations :

Various data science Venn diagrams. Image courtesy of Google Images. Source: https://sinews.siam.org/Details-Page/a-timely-focus-on-data-science

There seems to be no consensus on the data science skill set. Additionally, as the field evolves, shortcomings become obvious and new challenges arise. How can we describe this evolution?

The first wave of data scientists happend before data became big and before data science was actually a thing (pre-2010s): Statisticians and analysts who had always been around, doing a lot of what modern data scientists are doing, but accompanied by less hype.

Second wave: Large-scale data collection created a demand for smart minds who can work magic and turn all this big data into big money. Companies were still figuring out what kind of people to employ and often turned to science graduates. While the second wave data scientists did a lot right, their carefully crafted models often ended up as PoCs and failed to bring about actual change.

Now, at the end of the 2010s, amidst the hype around deep learning and AI, enter the third wave of data scientists : Experimenting and innovating, efficiently seeking out business value und bridging the deployment gap to create great data products. What skills are required here?

The skill portfolio of the third wave data scientist.