---
esco_uri: http://data.europa.eu/esco/skill/74e49482-75e8-4637-bdb1-fff868d02f7a
esco_label: "scientific literature"
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
---

# scientific literature — Data Scientist

For a Data Scientist, 'scientific literature' isn’t about *producing* research papers (though that can happen!). It's about rapidly and critically consuming existing work to inform your projects. Daily tasks involve identifying relevant papers on algorithms (e.g., new transformer architectures), statistical methods (e.g., causal inference techniques), domain-specific knowledge (e.g., fraud detection in finance, protein folding in biology), or even best practices for data cleaning/validation. You'll use tools like Google Scholar, Semantic Scholar, arXiv, and specialized databases relevant to your industry.  It’s about understanding *why* certain approaches work, their limitations, and how they compare to alternatives – not just blindly applying them.

## Weitere Anreicherung

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