by Tingting Liu
Tan, J., & Liu, T. (2026). Working closely with data: Aspirations and constraints of Chinese data scientists. Big Data & Society, 13(1). https://doi.org/10.1177/20539517261424165 (Original work published 2026)
What drew me to studying data scientists was their unique position in digital economy. As a cultural anthropologist deeply engaged with labour cultures, I have spent years researching factory workers, livestreamers, white-collar employees, and police officers. When my co-author Jingxin Tan began her master’s project on digital labour, our interests converged on this newly emerging profession.
In both China and Ameria, this profession emerged around the same time, to a certain degree reflecting the intense technological competition between the two countries. In English, it is usually called “data scientist,” while in China it is widely known as “algorithm engineer”. On Chinese recruitment platforms, the two labels often appear side by side, and the scope of work they cover has been constantly shifting, reflecting how the role continues to evolve. Data scientists’ everyday work revolves around collecting and cleaning massive datasets, building and optimizing machine-learning models, running endless experiments, and translating algorithmic results into business decisions. In their own everyday language, they often jokingly describe this process as liandan (“alchemy”), capturing the sense of repeatedly tweaking parameters and hoping for breakthroughs.
Many data scientists know that the systems they build may replace other digital workers. Yet they often interpret this as proof that everyone must keep learning—or be left behind. This belief reflects how deeply they internalise the logic of the tech industry. At the same time, they joke about their own futures: “After 35, I’ll deliver food,” or “Maybe I’ll sell houses.” These jokes reveal both confidence and anxiety. What readers can take away is this: behind the glossy image of AI innovation are workers caught between high pay, professional pride, and fragile long-term prospects. Studying them helps us better understand the human costs of data-driven capitalism—and why optimism in tech can be both empowering and cruel.
