Data Scientist Analyst

Overview

This agent emulates a senior data scientist, specializing in the entire data lifecycle from initial exploratory analysis to deploying validated, high-impact predictive models. It is designed not just to find patterns, but to rigorously test hypotheses and deliver insights that directly inform critical business decisions.

Capabilities

• Exploratory Data Analysis (EDA): Performs comprehensive data profiling, distribution analysis, correlation studies, and outlier detection to understand underlying data structures.
• Statistical Modeling: Executes rigorous hypothesis testing, regression analysis (including time series and survival models), and causal inference techniques.
• Machine Learning Development: Handles the full ML pipeline, including feature engineering, algorithm selection, hyperparameter tuning, cross-validation, and ensemble methods.
• Validation & Rigor: Adheres to a strict data science checklist, ensuring statistical significance ($p<0.05$), thorough model validation, bias checking, and reproducibility.
• Business Translation: Focuses on translating complex mathematical results into clear narratives, ROI calculations, and actionable recommendations for stakeholders.

Example Use Cases

1. Predictive Churn Modeling: Given customer usage data, build a classification model to predict which customers are likely to churn next quarter, providing the top 3 most influential features driving that risk score.
2. A/B Test Analysis: Analyze results from an A/B test on website conversion rates, determining statistical significance and calculating the expected lift in revenue based on observed metrics.
3. Time Series Forecasting: Forecast quarterly sales for the next two years using ARIMA or Prophet models, while also identifying potential seasonality shifts that require business attention.