---
esco_uri: http://data.europa.eu/esco/skill/cc7370dd-69fa-4c67-a96f-d4d135d38700
esco_label: "data quality assessment"
relation: optional
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
---

# data quality assessment — Data Scientist

As a Data Scientist, 'Data Quality Assessment' isn’t about ticking boxes on a checklist; it's woven into *every* stage of a project. Daily tasks involve profiling incoming datasets – using tools like Pandas `describe()`, Great Expectations, or even SQL queries to quickly identify missing values, outliers, inconsistent formats (dates, currencies), and unexpected distributions. You're not just finding errors, but understanding *why* they exist - is it a data entry issue, a sensor malfunction, or a flaw in the data pipeline? This informs how much effort cleansing requires versus potentially excluding problematic data points.

A core part of assessment is defining 'quality' relative to the project goal. A 95% accurate customer address might be fine for marketing blasts but unacceptable for fraud detection. You’ll build custom quality metrics (e.g., percentage of valid email addresses, range of acceptable values for a sensor reading) and track them over time using data observability tools like Monte Carlo or Soda.  Expect to spend significant time writing validation rules – these aren't one-offs; they become part of your automated data pipelines.

The biggest pitfalls? Assuming data is 'clean enough', relying solely on summary statistics without diving into individual records, and failing to document quality issues *and* the decisions made about handling them.  Good Data Quality Assessment isn’t just finding problems, it's creating a repeatable process for identifying and mitigating risks before they impact model performance or business insights. It also means communicating those findings clearly – not just technical details but the potential implications for stakeholders.

What does 'good' look like? A proactive approach where data quality checks are automated as part of ETL processes, clear dashboards showing key quality metrics, well-documented data lineage tracing issues back to their source, and a collaborative relationship with data engineers to fix root causes. It’s about building trust in the data *and* demonstrating that you understand its limitations.

## Weitere Anreicherung

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