Verified65% confidenceFactExact time
In real industrial environments, data scientists typically spend 60%-80% of their time on data cleaning and feature engineering, rather than model tuning.
3
Sources
65%
Confidence
Long-term
Relevance
8/2/2026
First Seen
Sources
From Zero to AI Engineer: A Real Growth Path from a Student's Resume
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UnverifiedIn real-world work, data scientists often need to spend 60% to 80% of their time on data collection, cleaning, and problem definition.90% similarUnverified据CrowdFlower 2016年行业调查及后续研究，数据科学家约60%-80%的工作时间消耗在数据清洗与预处理阶段82% similarUnverifiedFeature engineering typically accounts for 60–80% of the workload in data science projects.75% similarUnverified在实际工业环境中，数据理解、特征工程、评估指标选择、模型监控和迭代往往占据80%以上的工作量，模型选择只是很小的一环70% similarVerifiedIn actual machine learning projects, data preprocessing typically accounts for 60%–80% of the workload.69% similar
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