---
esco_uri: http://data.europa.eu/esco/skill/a57a54b6-2f2e-43e4-9621-b52f4a63cb08
esco_label: "LDAP"
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
---

# LDAP — Data Scientist

As a Data Scientist, 'LDAP' isn’t about *writing* LDAP queries as an end in itself; it's about understanding where user & system metadata lives and how to access it for feature engineering or data enrichment. Think of scenarios like building fraud detection models – you might need attributes from Active Directory (often accessed via LDAP) like employee role, department, creation date, last login time, etc., to supplement transaction data. Or, in personalization tasks, understanding user groups/permissions can inform recommendation engines. You'll rarely be crafting complex `search` filters directly; instead, you’ll likely interact with Python libraries (like `python-ldap`) or APIs that abstract the LDAP interaction.

## Weitere Anreicherung

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