---
esco_uri: http://data.europa.eu/esco/skill/979cf728-896f-4f2c-8fa8-2698ca0d4930
esco_label: "publish academic research"
relation: essential
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# publish academic research — Data Scientist

For a Data Scientist, 'publishing academic research' isn’t about aiming for tenure; it’s about establishing *credibility* and staying at the cutting edge of rapidly evolving techniques. It means going beyond applying existing algorithms – actively identifying novel problems where data science can provide unique solutions, developing new methodologies (e.g., a more efficient deep learning architecture for time series forecasting, or a bias-mitigation technique tailored to specific datasets), and rigorously documenting those advancements. This often manifests as conference papers (NeurIPS, ICML, KDD are common targets) rather than journal articles initially, focusing on practical results and reproducible code.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
