---
esco_uri: http://data.europa.eu/esco/skill/4f5c1208-62fd-4e5a-a51e-306c06947e11
esco_label: "manage findable accessible interoperable and reusable data"
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 findable accessible interoperable and reusable data — Data Scientist

For a Data Scientist, 'managing FAIR data' isn’t just about ticking boxes; it's woven into the entire project lifecycle. Daily tasks involve not simply *using* datasets, but actively assessing and improving their FAIRness. This means documenting data lineage (where did it come from?), creating clear metadata schemas – beyond basic column names – that describe variables, units, and potential biases, and choosing storage solutions (data lakes, warehouses) that support versioning and access control. Think about building a model on customer data: you need to track not just the features used *in* the model, but also how those features were derived from raw sources, who had access, and any transformations applied. 

Tools frequently used include data catalogs (like Amundsen or DataHub) for discovery, metadata management tools (e.g., CKAN), version control systems (Git for data/code alongside the data itself!), and increasingly, automated data quality & observability platforms. A common pitfall is treating data as disposable – focusing solely on model performance *now* without considering long-term usability or reproducibility. Another is assuming everyone understands your custom data formats; interoperability requires standardized formats like Parquet or JSON Schema whenever possible. 

Good looks like being able to quickly locate relevant datasets for new projects, confidently understanding their limitations and provenance, and easily sharing derived datasets with colleagues (or even publishing them publicly if appropriate). It also means building pipelines that automatically generate metadata and enforce data quality rules.  Crucially, it's about enabling *others* – future you included! – to build upon your work without spending weeks deciphering undocumented data sources.

Finally, remember the 'as open as necessary' part of FAIR. Data Scientists often deal with sensitive information (PII, financial data). This competence isn’t just about openness; it’s about implementing appropriate access controls, anonymization techniques, and adhering to privacy regulations *while still* maximizing data usability for legitimate analytical purposes.

## Weitere Anreicherung

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