---
esco_uri: http://data.europa.eu/esco/skill/00b9a3aa-7070-4bb5-8020-f228a97cf42f
esco_label: "draft scientific or academic papers and technical documentation"
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
---

# draft scientific or academic papers and technical documentation — Data Scientist

As a Data Scientist, 'drafting scientific/academic papers & technical documentation' isn’t about publishing groundbreaking research *every* week. It's primarily about clearly communicating complex analyses to both technical and non-technical audiences. Daily tasks include writing reports summarizing model performance (accuracy, precision, recall), documenting data pipelines for reproducibility, creating ‘explainable AI’ summaries of model decisions for stakeholders, and crafting presentations explaining insights derived from data. Think less 'peer-reviewed journal' and more 'persuasive business case supported by evidence'.

Tools frequently used go beyond just Word/Google Docs. Expect to use Markdown (for READMEs & documentation), Jupyter Notebooks (combining code, results, and narrative), potentially LaTeX for more formal reports or publications, and visualization tools like Tableau or Power BI to *show* the data supporting your claims. Version control with Git is crucial – documenting changes alongside code ensures traceability. A key skill is translating statistical jargon into plain language; a beautifully complex model is useless if no one understands its output.

The biggest pitfalls? Overly technical writing that alienates stakeholders, insufficient documentation leading to ‘black box’ models nobody trusts or can maintain, and failing to clearly state assumptions & limitations of your analysis.  Avoid 'data storytelling' that prioritizes narrative over factual accuracy. Another common issue is neglecting to document data sources, cleaning steps, and feature engineering – making replication impossible.

Good looks like: documentation that allows another Data Scientist (or even *you* six months later) to understand, reproduce, and extend your work; reports that concisely convey key findings with supporting visuals; presentations that clearly articulate the business value of your insights. It's about building trust through transparency and enabling data-driven decision making across the organization.

## Weitere Anreicherung

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