# How do Data Science Workers Collaborate? Roles, Workflows, and Tools

arXiv: https://arxiv.org/abs/2001.06684 · HTML: https://mlsanity.com/paper/2001.06684/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2020.

## Built on by (most cited first, 30 of 64)

- [MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming](https://mlsanity.com/paper/2310.06500/) (2023)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits](https://mlsanity.com/paper/2205.06922/) (2022)
- [AutoDS: Towards Human-Centered Automation of Data Science](https://mlsanity.com/paper/2101.05273/) (2021)
- [How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study](https://mlsanity.com/paper/2101.06098/) (2021)
- [Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition](https://mlsanity.com/paper/2407.02651/) (2024)
- [Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata](https://mlsanity.com/paper/2206.02923/) (2022)
- [Operationalizing Machine Learning: An Interview Study](https://mlsanity.com/paper/2209.09125/) (2022)
- [Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities](https://mlsanity.com/paper/2103.16007/) (2021)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop](https://mlsanity.com/paper/2101.04296/) (2021)
- [Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development](https://mlsanity.com/paper/2110.02932/) (2021)
- [Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences](https://mlsanity.com/paper/2310.04621/) (2023)
- [How Much Automation Does a Data Scientist Want?](https://mlsanity.com/paper/2101.03970/) (2021)
- [GAM Changer: Editing Generalized Additive Models with Interactive Visualization](https://mlsanity.com/paper/2112.03245/) (2021)
- [Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work](https://mlsanity.com/paper/2203.11085/) (2022)
- [Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design](https://mlsanity.com/paper/2303.02884/) (2023)
- [Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice](https://mlsanity.com/paper/2306.06542/) (2023)
- [Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability](https://mlsanity.com/paper/2204.06425/) (2022)
- [Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models](https://mlsanity.com/paper/2102.00036/) (2021)
- [Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?](https://mlsanity.com/paper/2109.08131/) (2021)
- [Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AI](https://mlsanity.com/paper/2309.12327/) (2023)
- [Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations](https://mlsanity.com/paper/2403.06009/) (2024)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (2022)
- [Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling](https://mlsanity.com/paper/2304.08366/) (2023)
- [AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science](https://mlsanity.com/paper/2506.13992/) (2025)
- [HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks](https://mlsanity.com/paper/2104.01002/) (2021)
- [Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference](https://mlsanity.com/paper/2404.03085/) (2024)
- [The Evolution of LLM Adoption in Industry Data Curation Practices](https://mlsanity.com/paper/2412.16089/) (2024)

## Abstract (arXiv)

Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.