Exact public version
senior-data-scientist v1
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.
335ff6c1c93f48d9bd64b928ce85914048df5c5d468917daae9ae2124716f249Package contents
Manifest
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.
18086267412e93cdac265bea537c21b50b92a999d6a2e894428702f62368ac18- SKILL.md9,404 bytes
- references/experiment_design_frameworks.md1,435 bytes
- references/feature_engineering_patterns.md1,435 bytes
- references/statistical_methods_advanced.md1,435 bytes
- scripts/experiment_designer.py2,784 bytes
- scripts/feature_engineering_pipeline.py2,827 bytes
- scripts/model_evaluation_suite.py2,797 bytes