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senior-data-scientist

Design experiments and build machine learning pipelines with Python.

Updated Nov 3, 2016
Search Tags:#data science#machine learning#feature engineering#statistics#causal inference#mlflow#ab testing
One-click install
npx skills add https://github.com/xleliberty/mydotfiles --skill senior-data-scientist-xleliberty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: senior-data-scientist
Source: https://github.com/xleliberty/mydotfiles/tree/main/.config/.claude/plugins/cache/claude-code-skills/engineering-skills/2.1.2/senior-data-scientist
Command: npx skills add https://github.com/xleliberty/mydotfiles --skill senior-data-scientist-xleliberty

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, pandas, scikit-learn, xgboost, mlflow, statsmodels, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill addresses the complexity of designing rigorous experiments and building reliable machine learning pipelines, ensuring that data-driven decisions are backed by statistically sound methodologies and production-ready code.

Core Features & Use Cases

  • Experiment Design: Automates sample size calculation, A/B test analysis with confidence intervals, and causal inference using Difference-in-Differences.
  • ML Pipelines: Provides robust feature engineering templates and cross-validated model evaluation suites for classification and regression tasks.
  • Use Case: Use this skill to validate a new product feature's impact via a controlled A/B test or to build a production-grade XGBoost pipeline that includes automated feature scaling and MLflow experiment tracking.

Quick Start

Use the senior-data-scientist skill to calculate the required sample size for an A/B test with a 10 percent baseline conversion rate and a 5 percent minimum detectable effect.

Frequently Asked Questions about senior-data-scientist

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

FAQPage Schema
How do I calculate sample size for an A/B test with a specific baseline conversion rate?▼

You can calculate the required sample size for an A/B test by providing the baseline conversion rate and the minimum detectable effect. This skill automates the statistical calculation to ensure your experiment design is statistically sound and production-ready.

How do I build a cross-validated machine learning pipeline using XGBoost and Scikit-learn?▼

Building a cross-validated XGBoost pipeline with Scikit-learn requires robust feature engineering and model evaluation suites. This skill provides production-ready templates that automate feature scaling, model training, and MLflow experiment tracking for reproducible results.

Can I perform causal inference and Difference-in-Differences analysis using Statsmodels?▼

Yes, you can perform causal inference using Difference-in-Differences with Statsmodels. This skill supports advanced statistical modeling to validate the impact of new features, ensuring data-driven decisions are backed by rigorous causal analysis.

Does this data science skill integrate with MLflow for experiment tracking?▼

Yes, this skill integrates with MLflow to ensure reproducible and scalable data science workflows. It automatically logs experiment parameters, metrics, and artifacts during machine learning pipeline development and cross-validated model evaluation.

What is the best way to automate feature engineering for production-grade machine learning systems?▼

The best way to automate feature engineering is using robust templates within a cross-validated pipeline. This skill provides production-grade feature engineering suites for classification and regression tasks, ensuring scalable and reproducible data processing.

Do I need Python and Pandas to run statistical modeling and A/B test analysis?▼

Yes, you need Python with Pandas, Scikit-learn, XGBoost, and Statsmodels to run this skill. These dependencies are required to perform advanced statistical modeling, experiment design, and ensure reproducible data science workflows.

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