---
esco_uri: http://data.europa.eu/esco/skill/4cd85845-f31c-4306-994d-e12d45114d83
esco_label: "teach in academic or vocational contexts"
relation: optional
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
---

# teach in academic or vocational contexts — Data Scientist

For a Data Scientist, 'teaching in academic or vocational contexts' isn’t about formal lecturing (though it *can* be!). More often, it manifests as knowledge sharing *within* teams and with stakeholders. This means explaining complex statistical concepts (like p-values, regularization, or model evaluation metrics) to non-technical audiences – product managers needing to understand A/B test results, marketing wanting insights from customer segmentation, or engineers implementing a new machine learning pipeline. Tools used are less about chalkboards and more about clear documentation (Markdown, Jupyter notebooks), presentations (PowerPoint, slides with code snippets), and interactive dashboards (Tableau, Power BI) that *demonstrate* the 'why' behind the data.

## Weitere Anreicherung

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