---
esco_uri: http://data.europa.eu/esco/skill/edebd83d-35f6-4ed5-a940-6c203d178c01
esco_label: "data science"
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
---

# data science — Data Scientist

For a Data Scientist, 'Data Science' isn’t just *knowing* machine learning algorithms – it’s applying them pragmatically to messy, real-world business problems. Daily work revolves around translating vague requests (like “improve customer retention”) into quantifiable questions, then identifying and accessing relevant data sources. This means heavy use of SQL for database querying, Python/R for analysis & modeling (using libraries like scikit-learn, pandas, TensorFlow/PyTorch), and often cloud platforms (AWS, Azure, GCP) for scalable processing. A big part is *data wrangling* – cleaning, transforming, and validating data which frequently takes 60-80% of the time.

## Weitere Anreicherung

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