Unlocking the Full Potential of Data Science Requires Tabular Foundation Models, Agents, and Humans

Tianji Cong, Julian Martin Eisenschlos, Daniel Gomm, Leo Grinsztajn, Andreas C Mueller, Anupam Sanghi, Jan-Micha Bodensohn, Vadim Borisov, Michael Cochez, Katharina Eggensperger, Floris Geerts, Myung Jun Kim, Andreas Kipf, Xue Li, Olga Ovcharenko, Paolo Papotti, Lennart Purucker, Sebastian Schelter, Immanuel Trummer, Gaël Varoquaux et al. (4 additional authors not shown)

Keywords: Automated Data Science, Agentic Systems, Tabular Foundation Models, Human-in-the-Loop Data Analysis, Human-in-the-Lead

Abstract:

Despite its vast potential, data science remains constrained by manual workflows and fragmented tools. Meanwhile, foundation models have transformed natural language and computer vision — and are beginning to bring similar breakthroughs to structured data, particularly the ubiquitous tabular data central to data science.
At the same time, there are strong claims that fully autonomous agentic data science systems will emerge.
We argue that, rather than replacing data scientists, the future of data science lies in a new paradigm that amplifies their impact: collaborative systems that tightly integrate agents and tabular foundation models (TFMs) with human experts.
In this paper, we discuss the potential and challenges of navigating the interplay between these three and present a research agenda to guide this disruption toward a more accessible, robust, and human-centered data science.

Submission Number: 401