---
esco_uri: http://data.europa.eu/esco/skill/68ef0e4d-5542-4e64-84f4-752cce5a3c22
esco_label: "statistical modeling techniques"
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
---

# statistical modeling techniques — Data Scientist

As a Data Scientist, 'statistical modeling techniques' isn’t about *knowing* formulas; it's about strategically choosing & applying the right one to answer a business question from data. Daily tasks involve things like building regression models (linear, logistic, polynomial) for prediction – forecasting sales, predicting customer churn, or estimating risk. You'll also use time series analysis (ARIMA, Prophet) for trend identification and future projections, clustering algorithms (k-means, hierarchical) to segment customers, and potentially more advanced techniques like Bayesian modeling when dealing with uncertainty or prior knowledge. It’s about translating a vague problem ('how can we improve customer retention?') into a quantifiable model.

## Weitere Anreicherung

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