•1 min read•from Data Science
DS interviews - Rant
Our take
Data Science (DS) interviews present a challenging landscape, often lacking the standardization seen in Software Development Engineer (SDE) or Machine Learning Engineer (MLE) processes. While SDEs can focus on Leetcode and system design, and MLEs follow a similar path, DS candidates face a confusing array of expectations. Different companies prioritize various skills—SQL and metrics at Meta, statistics at Google, and a mix of SQL and light MLE concepts at Amazon.
This is rant about how non standardized DS interviews are. For SDEs, the process is straight forward (not talking about difficulty). Grind Leetcode, and system design. For MLE, the process is straight forward again, grind Leetcode, and then ML system design. But for DS, goddamn is it difficult.
Meta -- DS is sql, experimentation, metrics; Google -- DS is stats primarily; Amazon - DS is MLE light, sql, leetcode; Other places have take home and data cleaning etc. How much can one prepare? Sometimes it feels like grinding leetcode for 6 months pays off so much more than DS in the longer run.
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