# JobHop: A Large-Scale Dataset of Career Trajectories

arXiv: https://arxiv.org/abs/2505.07653 · HTML: https://mlsanity.com/paper/2505.07653/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 11 of 11)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [LLMs Accelerate Annotation for Medical Information Extraction](https://mlsanity.com/paper/2312.02296/) (2023)
- [In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs Miss](https://mlsanity.com/paper/2402.10790/) (2024)
- [Career Path Prediction using Resume Representation Learning and Skill-based Matching](https://mlsanity.com/paper/2310.15636/) (2023)
- [CAREER: A Foundation Model for Labor Sequence Data](https://mlsanity.com/paper/2202.08370/) (2022)
- [LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models](https://mlsanity.com/paper/2309.09708/) (2023)
- [LABOR-LLM: Language-Based Occupational Representations with Large Language Models](https://mlsanity.com/paper/2406.17972/) (2024)
- [KARRIEREWEGE: A Large Scale Career Path Prediction Dataset](https://mlsanity.com/paper/2412.14612/) (2024)

## Built on by (most cited first, 3 of 3)

- [JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes](https://mlsanity.com/paper/2607.11715/) (2026)
- [STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling](https://mlsanity.com/paper/2607.11722/) (2026)
- [An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation](https://mlsanity.com/paper/2605.24155/) (2026)

## Abstract (arXiv)

Understanding labor market dynamics is essential for policymakers, employers, and job seekers. However, comprehensive datasets that capture real-world career trajectories are scarce. In this paper, we introduce JobHop, a large-scale public dataset derived from anonymized resumes provided by VDAB, the public employment service in Flanders, Belgium. Utilizing Large Language Models (LLMs), we process unstructured resume data to extract structured career information, which is then normalized to standardized ESCO occupation codes using a multi-label classification model. This results in a rich dataset of over 1.67 million work experiences, extracted from and grouped into more than 361,000 user resumes and mapped to standardized ESCO occupation codes, offering valuable insights into real-world occupational transitions. This dataset enables diverse applications, such as analyzing labor market mobility, job stability, and the effects of career breaks on occupational transitions. It also supports career path prediction and other data-driven decision-making processes. To illustrate its potential, we explore key dataset characteristics, including job distributions, career breaks, and job transitions, demonstrating its value for advancing labor market research.