5c03e07ded
Simpler story (slide decks unread -> three-bullet status email), proper Claude-chat look with avatars and bubbles on both sides. Canonical trigger prompt switched to English everywhere (homepage, architecture, both adapter generators rebuilt) - V7 consistency green, all checks pass. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PDKeXvpT6tENSvyQGLV1Uq
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esco_uri, esco_label, relation, onet_soc, source, confidence, qa_count, generator, generated
| esco_uri | esco_label | relation | onet_soc | source | confidence | qa_count | generator | generated |
|---|---|---|---|---|---|---|---|---|
| http://data.europa.eu/esco/skill/713fb616-118e-40bc-9366-4a69879a49d5 | implement data quality processes | essential | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
implement data quality processes — Data Scientist
As a Data Scientist, 'implementing data quality processes' isn’t about just finding errors; it's woven into every stage of a project. Daily tasks include profiling incoming datasets (using tools like Pandas describe(), Great Expectations, or AWS Deequ) to understand distributions and identify anomalies before modeling. You'll write validation rules – are ages negative? Are categorical values within expected ranges? – often automated as data pipelines using Airflow/Prefect. It also means actively monitoring model inputs for 'data drift', where the characteristics of incoming data change over time, impacting prediction accuracy. Think less ‘one-time fix’ and more ‘ongoing health check’.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).