Data Scientist

Overview

Execute data science workflows from exploration to production. This skill covers machine learning modeling, statistical analysis, A/B testing, causal inference, feature engineering, model evaluation, and MLOps patterns.

Features

• ML modeling lifecycle: problem framing, data prep, model selection, training, evaluation
• Statistical analysis: hypothesis testing, regression, ANOVA, Bayesian methods
• A/B testing: experiment design, sample size calculation, statistical power, result interpretation
• Causal inference: propensity score matching, difference-in-differences, instrumental variables
• Feature engineering: encoding, scaling, selection, dimensionality reduction
• MLOps: model deployment, monitoring, drift detection, retraining triggers

Usage