---
esco_uri: http://data.europa.eu/esco/skill/39f67488-cfd3-4370-8d7e-da98163102d9
esco_label: "evaluate research activities"
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
---

# evaluate research activities — Data Scientist

As a Data Scientist, 'evaluating research activities' isn’t about judging academic papers in the traditional sense – it’s deeply embedded in *every* project. It manifests as critically assessing the validity of data sources (is this sensor reliable? is this web-scraped data representative?), evaluating the appropriateness of chosen modeling techniques for a given business problem, and questioning assumptions baked into feature engineering. You'll routinely review code written by colleagues – not just for bugs, but to understand *why* they approached a problem that way, if alternative methods were considered, and whether their evaluation metrics truly reflect success. Think 'code review + model validation + data quality assessment', all rolled into one continuous process.

## Weitere Anreicherung

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