---
esco_uri: http://data.europa.eu/esco/skill/505e4ef3-7ce4-437d-b7b4-5c608f71c258
esco_label: "build recommender systems"
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
---

# build recommender systems — Data Scientist

As a Data Scientist building recommender systems isn't just about applying algorithms; it’s about *solving business problems* through personalized experiences. Daily tasks often involve translating vague requests like 'increase user engagement' into concrete recommendation goals (e.g., predict click-through rate on articles, suggest relevant products). You'll spend significant time data wrangling – cleaning and transforming user interaction data (clicks, purchases, ratings) alongside item metadata (descriptions, categories). A large chunk is experimentation: rapidly prototyping different algorithms (collaborative filtering, content-based, hybrid approaches), evaluating performance with offline metrics like precision/recall or NDCG, *and* crucially, designing and analyzing A/B tests to measure impact on key business KPIs. It's rarely a 'build it and forget it' scenario; models need continuous monitoring and retraining as user behavior evolves.

## Weitere Anreicherung

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