What statistical tests does this skill support?

It provides built-in workflows for two-proportion z-tests, sample size calculations, Bonferroni corrections, and Difference-in-Differences analysis.

Can this skill help with model deployment?

Yes, it includes best practices for model tracking with MLflow and provides templates for Docker and Kubernetes deployment.

What libraries does the Senior Data Scientist skill utilize?

It primarily uses the Python data stack, including NumPy, Pandas, Scikit-learn, XGBoost, Scipy, Statsmodels, and MLflow.

Does it handle imbalanced datasets?

Yes, the skill enforces the use of AUC-PR (Precision-Recall) alongside AUC-ROC and utilizes Stratified K-Fold cross-validation to manage class imbalance.

Senior Data Scientist

Name: Senior Data Scientist
Author: zhangzhang-111-i

byzhangzhang-111-i

0•

データサイエンスとML

Empowers Claude with advanced statistical modeling, experimental design, and production-grade machine learning workflows.

The Senior Data Scientist skill transforms Claude into a high-level technical partner for architecting end-to-end data systems. It provides standardized implementations for designing controlled A/B tests, building automated feature engineering pipelines, and conducting rigorous causal inference using Difference-in-Differences. By integrating industry best practices for model evaluation—such as AUC-PR analysis and SHAP values—alongside MLflow tracking, this skill ensures that data-driven decisions are backed by statistical rigor and reproducible code.

主な機能

01Production-grade feature engineering pipelines for structured tabular data.

02Causal inference tools for observational data using Difference-in-Differences (DiD).

03Comprehensive A/B testing suite with sample size calculation and significance testing.

04Standardized experiment tracking and model logging with MLflow.

050 GitHub stars

06Advanced model evaluation including AUC-PR, SHAP values, and overfitting detection.

ユースケース

01Designing and analyzing product experiments to measure feature lift and statistical significance.

02Performing causal analysis on historical datasets to translate findings into business strategy.

03Building and cross-validating predictive models with XGBoost and Scikit-learn.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zhangzhang-111-i/claude-skills111 senior-data-scientist

For use in Claude.ai and ChatGPT

主な機能

01Production-grade feature engineering pipelines for structured tabular data.

02Causal inference tools for observational data using Difference-in-Differences (DiD).

03Comprehensive A/B testing suite with sample size calculation and significance testing.

04Standardized experiment tracking and model logging with MLflow.

050 GitHub stars

06Advanced model evaluation including AUC-PR, SHAP values, and overfitting detection.

ユースケース

01Designing and analyzing product experiments to measure feature lift and statistical significance.

02Performing causal analysis on historical datasets to translate findings into business strategy.

03Building and cross-validating predictive models with XGBoost and Scikit-learn.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zhangzhang-111-i/claude-skills111 senior-data-scientist

For use in Claude.ai and ChatGPT