Quick overview
Covers statistical modeling and experiment design for production ML systems. Handles A/B testing with sample sizing and significance testing, feature engineering pipelines with Scikit-learn and XGBoost, cross-validated model evaluation with SHAP explanations, and causal inference via difference-in-differences. Works in Python, R, and SQL.
Bundles experiment design, feature engineering, model evaluation, and causal inference into one skill, so analysis stays statistically rigorous from design through deployment.
Common tasks
- Sizing an A/B test before launching a new checkout flow
- Building a churn prediction model with cross-validated evaluation
- Estimating the causal effect of a policy change on revenue
- Engineering lag and cyclical features from transaction timestamps
- Tracking and comparing model runs across experiments in MLflow
Install paths
Primary command
openclaw install alirezarezvani/senior-data-scientist
ClawHub installer
npx clawhub@latest install alirezarezvani/senior-data-scientist
OpenClaw CLI
openclaw skills install alirezarezvani/senior-data-scientist
Direct OpenClaw install
openclaw install alirezarezvani/senior-data-scientist
Skill metadata
- Category: Data & Analytics
- Language: Markdown
- Version: 2.1.1
- Security status: Benign
Review upstream source
The full public SKILL.md body is not directly fetchable for this entry right now, so this page is using the best available catalog metadata. Review the upstream source page for the latest files, version history, and security scan details: https://clawhub.ai/alirezarezvani/senior-data-scientist
