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senior-data-scientist

Design A/B tests with sample sizing and statistical tests across Python, R, and SQL pipelines.

Updated Apr 9, 2026
Search Tags:#analyze#experiments#mlflow#model-evaluation#causal-inference#feature-engineering#A/B-testing
One-click install
npx skills add https://github.com/Patasse97/claude-skills --skill senior-data-scientist-patasse97
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: senior-data-scientist
Source: https://github.com/Patasse97/claude-skills/tree/main/engineering-team/senior-data-scientist
Command: npx skills add https://github.com/Patasse97/claude-skills --skill senior-data-scientist-patasse97

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Enables production-grade data science workflows by structuring experiments, evaluation, and causal analysis to ensure reliable, measurable results.

Core Features & Use Cases

  • A/B testing design with sample sizing and statistical tests (z-test, Bonferroni adjustments) for robust decision making.
  • Causal inference and difference-in-differences analysis to quantify treatment effects on observational data.
  • Feature engineering pipelines and cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP) with MLflow tracking for reproducibility.
  • Use cases include deploying reliable experiments in product analytics, fraud detection, and predictive modeling pipelines.

Quick Start

Design and run a controlled experiment on your dataset using the senior-data-scientist workflows.

Frequently Asked Questions about senior-data-scientist

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design an A/B test with proper sample sizing and multiple hypothesis testing?▼

To design an A/B test with proper sample sizing, you calculate statistical power and apply multiple hypothesis testing adjustments like Bonferroni corrections. This ensures robust decision making and prevents false positives when evaluating experiments across your data pipelines.

Can I use MLflow tracking for cross-validated model evaluation with SHAP explanations?▼

Yes, you can use MLflow tracking alongside cross-validated model evaluation. This workflow logs evaluation metrics like AUC-ROC and AUC-PR while generating SHAP explanations, ensuring reproducibility and interpretability across your feature engineering pipelines.

Does this approach support data pipelines across Python, R, and SQL?▼

Yes, this experimentation and evaluation approach supports Python, R, and SQL data pipelines. It structures production-grade data science workflows to ensure reliable, measurable results across diverse analytics environments without lock-in.

What is the best way to evaluate predictive models in fraud detection pipelines?▼

The best way to evaluate predictive models in fraud detection pipelines is through cross-validated evaluation using AUC-ROC and AUC-PR metrics. Integrating MLflow tracking and SHAP explanations ensures your feature engineering results are reproducible and measurable.

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