Hiring Missteps
Many organizations mistakenly hire data scientists when they actually need data engineers or BI analysts, leading to wasted resources and failed projects. The demand for data engineers far exceeds that for data scientists, highlighting a shift in industry needs. Additionally, misguided decisions can stem from executives being influenced by popular trends or blog posts about machine learning, rather than a clear understanding of their project requirements.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
833: The 10 Reasons AI Projects Fail — with Dr. Martin Goodson
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