The article discusses the shifting landscape for data scientists and machine learning engineers in the age of large language models (LLMs). It emphasizes the importance of data science fundamentals in evaluating AI systems, addressing common pitfalls in metrics, experimental design, and data quality. The author argues that the core work of data scientists remains vital, even as their roles evolve.

• Off-the-shelf eval framework metrics often mislead teams; digging into your own data to find relevant metrics is what data scientists actually do.
• Using LLMs as judges without validating them against human labels is a common, risky shortcut.
• Synthetic test data that isn't grounded in real production logs leads to flawed experimental design and misleading results.
• Outsourcing labeling away from domain experts degrades data quality and undermines the whole evaluation process.