---
esco_uri: http://data.europa.eu/esco/skill/f7b071bf-4882-4cfc-ae0a-7e0e0ab94c61
esco_label: "conduct research across disciplines"
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
---

# conduct research across disciplines — Data Scientist

For a Data Scientist, 'conducting research across disciplines' isn’t about publishing academic papers – it’s about *effectively translating* business problems into solvable analytical questions that require knowledge beyond just statistics and coding. Daily this means understanding the nuances of whatever domain you're working in (marketing, finance, healthcare, etc.). You might be analyzing customer churn, but truly impactful work requires grasping marketing strategies, customer lifecycle models, or even behavioral economics principles *alongside* your technical skills. It’s about asking 'why' beyond just 'what'.

Typical tasks involve quickly learning the jargon and key concepts of a subject matter expert (SME), then identifying how data can illuminate their challenges. This often looks like reading industry reports, attending webinars outside your core skillset, or – crucially – *actively interviewing* SMEs to validate assumptions and understand context. Tools aren’t just Python/R; they include search engines for literature reviews, business intelligence platforms to explore existing knowledge, and communication tools (Slack, email) for iterative questioning of stakeholders.

A common pitfall is 'solutionizing' before fully understanding the problem. A Data Scientist might build a perfect model addressing the wrong question because they didn’t spend enough time understanding the underlying business logic or constraints. Another is assuming correlation equals causation without considering external factors specific to the domain. Good looks like being able to *articulate* how your analysis connects back to the original business need, and acknowledging limitations based on incomplete domain knowledge.

Ultimately, cross-disciplinary research for a Data Scientist isn’t about becoming an expert in everything; it's about being a fast learner, a skilled communicator, and someone who can synthesize information from diverse sources to deliver actionable insights. It's the bridge between raw data and real-world impact.

## Weitere Anreicherung

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