Using Codex for Data Science: Parallel Agents Tackle Housing, Schools, and Churn
gabrielchua · x · 2026-08-05
Developer Gabriel Chua demonstrated how to integrate Codex into data science workflows. During a snippet from a recent talk, he fed Codex three distinct datasets: housing prices, primary school admissions, and customer churn, assigning specific business tasks for each.
- Housing Data: Prompted to merge housing and amenities data, build a resale-price dashboard, and deploy it with an ML-based calculator and API.
- Admissions Data: Transformed raw school data into a presentation complete with generated images and speaker notes.
- Churn Data: Tasked with comparing different churn models and explaining the difference between ROC AUC and PR AUC in plain English.
Chua also highlighted Codex's multi-agent capabilities, noting that a single thread can spawn and message another to handle interactive visualizations in parallel while other tasks continue. Users can steer the process midway, set goals, and ask the model to explain its operations.
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