Working closely with data: Aspirations and constraints of Chinese data scientists

This study examines how data scientists, locally known as suanfa gongchengshi , employed by major Chinese infotech companies navigate their professional identities and aspirations amid the expansive process of datafication within and beyond the tech industry. Drawing on interviews with 23 data scientists from leading firms such as Tencent, Alibaba, and ByteDance, alongside a critical discourse analysis of dominant narratives surrounding the profession, we identify three key dynamics. First, data scientists cultivate a datafied professional habitus through continuous algorithmic experimentation, model optimization, and real-time feedback interpretation, resulting in an “alchemy-like” engagement with data. Second, they derive immediate professional and emotional rewards from high salaries, technical achievements, and adaptability, which sustain resilience and meaning in daily work. Third, their long-term aspirations for innovation and transformative roles are systematically constrained, as monopolized access to computational resources, research opportunities, and creative autonomy by tech giants reduces them to implementers of preexisting models. These findings highlight how data scientists both shape and are constrained by the data-driven infrastructures they sustain, revealing the complex interplay between professional achievement, structural limits, and the reproduction of technological hierarchies.

Authors

Institutions

Publication Details

Journal
Big Data & Society
Published
2026-02-13
DOI
https://doi.org/10.1177/20539517261424165
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Working closely with data: Aspirations and constraints of Chinese data scientists

Jingxin Tan, Tingting Liu
Big Data & Society
Ethics and Social Impacts of AI
article

Working closely with data: Aspirations and constraints of Chinese data scientists

Jingxin Tan, Tingting Liu
article en

Abstract

This study examines how data scientists, locally known as suanfa gongchengshi , employed by major Chinese infotech companies navigate their professional identities and aspirations amid the expansive process of datafication within and beyond the tech industry. Drawing on interviews with 23 data scientists from leading firms such as Tencent, Alibaba, and ByteDance, alongside a critical discourse analysis of dominant narratives surrounding the profession, we identify three key dynamics. First, data scientists cultivate a datafied professional habitus through continuous algorithmic experimentation, model optimization, and real-time feedback interpretation, resulting in an “alchemy-like” engagement with data. Second, they derive immediate professional and emotional rewards from high salaries, technical achievements, and adaptability, which sustain resilience and meaning in daily work. Third, their long-term aspirations for innovation and transformative roles are systematically constrained, as monopolized access to computational resources, research opportunities, and creative autonomy by tech giants reduces them to implementers of preexisting models. These findings highlight how data scientists both shape and are constrained by the data-driven infrastructures they sustain, revealing the complex interplay between professional achievement, structural limits, and the reproduction of technological hierarchies.

Big Data & SocietyVol. 13(1)
University of Technology Sydney (AU), The University of Queensland (AU)
Industry, innovation and infrastructure
Openalex Percentile: Top 59%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.