---
esco_uri: http://data.europa.eu/esco/skill/a80fb090-63f4-4b05-83a5-2f090deb7757
esco_label: "manage data collection systems"
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
---

# manage data collection systems — Data Scientist

As a Data Scientist, 'managing data collection systems' isn’t about *physically* collecting data – it’s about architecting the pipeline that *feeds* your models. Daily tasks involve defining what data is needed for specific projects (feature engineering starts here!), then designing how to reliably acquire it. This means specifying APIs to pull from, writing scripts to scrape web data ethically, setting up event tracking with tools like Google Tag Manager or Mixpanel, and configuring database queries/ETL processes. You're constantly evaluating if the incoming data *actually* answers your research questions – is the granularity right? Are there biases creeping in? It’s a lot of SQL, Python (often with libraries like `requests`, `BeautifulSoup`, `pandas`), cloud storage services (AWS S3, Azure Blob Storage), and potentially message queues (Kafka, RabbitMQ).

## Weitere Anreicherung

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