---
esco_uri: http://data.europa.eu/esco/skill/9fc400ac-8ed2-4aff-915f-aa4f2609ec7b
esco_label: "multidisciplinary research"
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
---

# multidisciplinary research — Data Scientist

For a Data Scientist, 'multidisciplinary research' isn’t about *doing* all the disciplines, but skillfully *integrating* knowledge from them to frame problems and interpret results. Daily this means going beyond purely statistical modeling. You might be working with marketing teams (understanding customer behavior), engineers (grasping data collection limitations & system architecture), or domain experts like biologists/financial analysts (understanding the 'why' behind the numbers). A typical task is translating a business question – “Why are sales down?” – into a researchable hypothesis that requires not just model building, but also understanding of market trends, competitor actions, and potentially even qualitative data from customer interviews. Tools used aren’t limited to Python/R; they include communication platforms (Slack, presentations) for knowledge sharing and visualization tools to convey complex findings to non-technical stakeholders.

## Weitere Anreicherung

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