--- title: The Death of model.fit(): What Data Scientists Actually... description: A data scientist at monday.com reflects on joining an AI agent team where there was no model to train, no Python, and no traditional ML workflow. The post... canonical: https://daily.dev/posts/the-death-of-model-fit-what-data-scientists-actually-do-in-the-age-of-ai-agents-seweaauf7 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The Death of model.fit(): What Data Scientists Actually Do in the Age of AI Agents | daily.dev og:description: A data scientist at monday.com reflects on joining an AI agent team where there was no model to train, no Python, and no traditional ML workflow. The post... og:url: https://daily.dev/posts/the-death-of-model-fit-what-data-scientists-actually-do-in-the-age-of-ai-agents-seweaauf7 og:image: https://api.daily.dev/og/posts/seweaAUF7.png og:image:alt: The Death of model.fit(): What Data Scientists Actually Do in the Age of AI Agents og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The Death of model.fit(): What Data Scientists Actually Do in the Age of AI Agents **[monday Engineering](https://daily.dev/sources/monday)** · 13 min read · 1 upvotes · 0 comments ## Summary A data scientist at monday.com reflects on joining an AI agent team where there was no model to train, no Python, and no traditional ML workflow. The post argues that the data scientist's role in the agentic era shifts from model training to systematic evaluation and quality ownership. Key responsibilities include building error taxonomies from agent traces, curating golden datasets with real production failures, calibrating LLM-as-judge systems using inter-rater agreement metrics, and creating deterministic graders for structured outputs. The author introduces 'Evaluation-Driven Development' as the new feedback loop replacing model.fit(), and warns against the 'sprint velocity trap' where teams confuse shipping with improving. Context engineering is positioned as the new feature engineering, and the core data science value is framed as language-agnostic methodological rigor applied to understanding system behavior through data. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [From Data Scientist to AI Architect](https://daily.dev/posts/from-data-scientist-to-ai-architect-liz4og9ai) · Towards Data Science · 3 upvotes · 0 comments - [The Factory Model: How Coding Agents Changed Software Engineering](https://daily.dev/posts/the-factory-model-how-coding-agents-changed-software-engineering-eavrwedl9) · Addy Osmani · 64 upvotes · 2 comments - [Teaching Agents about Performance insights](https://daily.dev/posts/teaching-agents-about-performance-insights-i71bkunz0) · Web Performance Calendar · 3 upvotes · 0 comments - [The hidden skills behind the AI engineer](https://daily.dev/posts/the-hidden-skills-behind-the-ai-engineer-lx22hfedo) · InfoWorld · 0 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#context-engineering](https://daily.dev/tags/context-engineering), [#data-science](https://daily.dev/tags/data-science), [#llm](https://daily.dev/tags/llm), [#prompt-engineering](https://daily.dev/tags/prompt-engineering) [View this post on daily.dev](https://daily.dev/posts/the-death-of-model-fit-what-data-scientists-actually-do-in-the-age-of-ai-agents-seweaauf7) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"The Death of model.fit(): What Data Scientists Actually Do in the Age of AI Agents","url":"https://daily.dev/posts/the-death-of-model-fit-what-data-scientists-actually-do-in-the-age-of-ai-agents-seweaauf7","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-death-of-model-fit-what-data-scientists-actually-do-in-the-age-of-ai-agents-seweaauf7"},"datePublished":"2026-03-23T10:38:31.824Z","dateModified":"2026-04-23T02:15:25.420Z","description":"A data scientist at monday.com reflects on joining an AI agent team where there was no model to train, no Python, and no traditional ML workflow. 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