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Inside data science: Hackers and the making of a new profession

Data scientists dominated conversations in the big-data era, as industries, disciplines, and public agencies that had previously done without coding and math sought new specialists. But these new hires had no clear tasks and felt uneasy under the spotlight.

How did some scattered nerds and hackers turn mostly familiar and partly questionable ideas into a new profession?

In this talk, recorded at BESI on October 21, sociologist Philipp Brandt draws on the history of quantitative thought, a reflexive data science-of-data science exercise, and three years of observations of semi-public gatherings in New York City’s tech scene in the early 2010s to show how participants devised the technical machinery for seeing the world through datasets.

Ultimately, Brandt shows how the interplay of personal reflection, technical rigor, and collective scrutiny gave the big-data era, for better or for worse, a human face.

About the speaker

Philipp Brandt is an assistant professor of sociology at Sciences Po, Paris, and a chercheur at the Centre de Sociologie des Organisations. Previously, he was a postdoctoral researcher at the University of Mannheim, Germany. He holds a Ph.D. in sociology from Columbia University in New York City.

In his research, Brandt develops computational approaches to study work trajectories and activities in emergent, precarious, and otherwise unstable settings. His projects address a series of complementary questions, such as how experts define their role and identity in the digital era, how street-level bureaucrats operate in a hostile political setting, and how immigrant workers array jobs and gigs into careers outside of established paths. In particular, he focuses on integrating ideas from qualitative research into analyses of large-scale behavioral records for making new sociological observations. His findings inform a larger argument about non-standard work by revealing how workers leverage gaps between their work’s underlying social arrangements and overt appearances for distinct gains.