•1 min read•from Data Science
Feels like DS hiring logic is starting to change because of AI
Our take
The landscape of data science hiring appears to be evolving, particularly with the rise of AI-driven tools like Litmetrics.ai, which emphasize real-world datasets and messy business scenarios over traditional coding tests. This shift suggests a growing recognition that modern data science roles require end-to-end analytical judgment, integrating AI into the decision-making process.
Been noticing new DS hiring products like Litmetrics.ai lately, which seems much more focused on real datasets and messy business cases than the classic coding-test format.
A lot of DS work today are more like to be end-to-end analytical judgment with AI in the loop. That feels like a different hiring target than the classic CodeSignal / HackerRank screening - pretty sure most DS have used them in interviews.
Curious what other people think. Is DS hiring actually changing on the assessment layer - to whether candidates can work through an real business problem, or putting AI language on top of the classic coding test & screening process is still the best way?
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#business intelligence tools#real-time data collaboration#real-time collaboration#rows.com#natural language processing for spreadsheets#financial modeling with spreadsheets#natural language processing#DS#AI#hiring#Litmetrics.ai#real datasets#analytical judgment#messy business cases#CodeSignal#HackerRank#business problems#assessment layer#coding-test format#screening process