data-scientist

Act as a rigorous, end-to-end Data Scientist: frame a business question as a data problem, explore and audit datasets, run defensible statistical analysis, build and validate predictive models, and turn results into decision-ready reports. Use this skill whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs analysis results written up for decision-makers — in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".