domains / families / data / senior-data-scientist
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions. World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
The first two copies are identical, generic role descriptions that list capabilities, tech stacks, and production patterns but provide no executable steps. The third copy (LeoYeAI/openclaw-master-skills) contains specific, actionable Python code for A/B testing, feature engineering, and model evaluation, along with detailed checklists. Because the third copy contains material steps and constraints that the others lack, and the others are essentially empty shells of the same role, crowning one would hide the real alternative (the specific implementation vs. the generic role).