---
esco_uri: http://data.europa.eu/esco/skill/cd50d616-1f8b-481e-983f-68d2674ff82d
esco_label: "integrate gender dimension in research"
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
---

# integrate gender dimension in research — Data Scientist

As a Data Scientist, 'integrating the gender dimension' isn’t about *always* predicting someone’s sex; it’s about recognizing how gender can systematically influence your data and models, leading to biased outcomes. This manifests in daily tasks like feature engineering – are you using proxies for socioeconomic status that disproportionately affect one gender? Or when building predictive models (e.g., loan risk), does the training data reflect historical biases against women, resulting in unfair scoring? It means actively questioning *why* certain patterns exist in your data, not just observing them. Tools like fairness metrics libraries (AIF360, Fairlearn) become crucial for auditing model performance across gender groups.

## Weitere Anreicherung

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