Recent advances in artificial intelligence (AI), particularly generative AI, have increased public concern regarding the potential displacement of data science professionals. Popular discourse frequently frames AI as a direct substitute for analytical labor, fueling fears of widespread job loss. However, jobs are bundles of tasks, and technological change often reshapes work internally rather than removing occupations outright. This study adopts a task oriented perspective to examine whether AI diffusion corresponds with job displacement or, instead, task reallocation within the data science profession. Using a comparative analysis of U.S. based Data Scientist job postings from a pre-diffusion baseline derived from a Glassdoor scraped dataset (published in the late 2010s) and a post-diffusion dataset of 2023 postings curated by Luke Barousse, this paper analyzes changes in task and skill related language as proxies for evolving employer expectations. The analysis indicates substantial shifts in the composition of emphasized skills and responsibilities rather than a collapse in demand. In particular, several traditional tools and routine signals have declined in relative share, while signals related to orchestration and modern production workflows have increased. These findings are consistent with a task reallocation framework and motivate task level measurement as a complement to occupation level narratives.

Using two public job postings datasets, this paper documents shifts in the composition of tasks and skill signals within Data Scientist roles between a pre-diffusion baseline and a 2023 post-diffusion snapshot. The evidence is more consistent with task reallocation than job displacement: core analytical skills remain widespread, while signals related to orchestration and production workflows gain relative prominence. Task level measurement provides a practical complement to occupation level narratives and offers a more grounded way to discuss AI’s impact on analytical work.

Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from U.S. labor markets. Journal of Political Economy, 128(6), 2188-2244. https://doi.org/10.1086/705716 Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3-30. https://doi.org/10.1257/jep.29.3.3 Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279-1333. https://doi.org/10.1162/003355303322552801 Barousse, L. (2023). data_jobs: Real-world data analytics job postings from 2023 [Data set]. Hugging Face. https://huggingface.co/datasets/lukebarousse/data_jobs Brynjolfsson, E., Mitchell, T., & Rock, D. (2018). What can machines learn, and what does it mean for occupations and the economy? AEA Papers and Proceedings, 108, 43-47. https://doi.org/10.1257/pandp.20181019 Kaggle. (n.d.). Data Scientist Jobs (Glassdoor-scraped postings) [Data set]. https://www.kaggle.com/datasets/andrewmvd/data-scientist-jobs National Center for O*NET Development. (n.d.). O*NET OnLine: Data Scientists (15-2051.00). https://www.onetonline.org/link/summary/15-2051.00 U.S. Bureau of Labor Statistics. (n.d.). Data Scientists: Occupational Outlook Handbook. https://www.bls.gov/ooh/math/data-scientists.htm U.S. Bureau of Labor Statistics. (n.d.). Occupational Employment and Wage Statistics (OEWS): Data Scientists (15-2051). https://www.bls.gov/oes/2023/may/oes152051.htm

© 2025 International Journal of Engineering and Techniques (IJET).

Digital Object Identifier (DOI)

IJET assigns a unique DOI to every accepted article via Zenodo for persistent linking and citation. Your article’s DOI: https://doi.org/{{doi}}

Open DOI About Zenodo DOI

Close