---
esco_uri: http://data.europa.eu/esco/skill/2daec0e6-f0c1-43e8-8178-aedba99130ec
esco_label: "establish data processes"
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
---

# establish data processes — Data Scientist

For a Data Scientist, 'establish data processes' isn’t just about *running* algorithms; it's building the repeatable pipelines that *feed* those algorithms reliable data. Daily work involves tasks like scripting ETL (Extract, Transform, Load) jobs – often using Python with libraries like Pandas and PySpark – to pull data from various sources (databases, APIs, cloud storage). It means defining data quality checks (using tools like Great Expectations or Deequ), handling missing values/outliers, and ensuring consistent data formats.  It's about automating these steps so the same clean, prepared dataset is available for model training *and* ongoing monitoring without manual intervention.

## Weitere Anreicherung

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