Does this skill cover model deployment and MLOps?

Absolutely. It provides production patterns for model serving, A/B testing infrastructure, monitoring, and automated retraining pipelines.

Can it help with experiment design and A/B testing?

Yes, the skill includes advanced frameworks for designing experiments, calculating statistical power, and performing causal inference.

Which data science libraries does this skill support?

It includes deep expertise in Python (NumPy, Pandas, Scikit-learn, PyTorch), R, SQL, Spark, and MLOps tools like MLflow and Airflow.

Is this skill suitable for enterprise security requirements?

Yes, it incorporates best practices for data encryption, PII handling, and compliance with standards like GDPR and CCPA.

What is the Senior Data Scientist skill for Claude Code?

It is a specialized capability that provides Claude with expert guidance on statistical modeling, machine learning workflows, and production-grade data systems.

Senior Data Scientist

Name: Senior Data Scientist
Author: pur3v4d3r

bypur3v4d3r

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数据科学与机器学习

Empowers Claude to design rigorous statistical experiments, build predictive models, and implement production-grade MLOps pipelines.

关于

This skill transforms Claude into a high-level data scientist capable of handling the entire machine learning lifecycle, from advanced statistical modeling and causal inference to scalable model deployment. It provides domain-specific expertise in Python, R, and SQL, offering patterns for feature engineering, time-series analysis, and real-time inference systems. By integrating best practices for MLOps and DataOps, it ensures that data-driven decisions and AI models are built with the architectural rigor, security, and performance monitoring required for enterprise-grade environments.

主要功能

Advanced statistical modeling and experiment design frameworks
Causal inference and business intelligence analytics
Production-grade MLOps for model deployment and monitoring
1 GitHub stars
Automated feature engineering and scalable data processing pipelines
Real-time inference optimization and distributed computing expertise

使用场景

Designing and validating A/B tests for product feature rollouts
Building and deploying scalable predictive models using PyTorch or Scikit-learn
Architecting real-time inference systems with monitoring for model drift

关于

主要功能

Advanced statistical modeling and experiment design frameworks
Causal inference and business intelligence analytics
Production-grade MLOps for model deployment and monitoring
1 GitHub stars
Automated feature engineering and scalable data processing pipelines
Real-time inference optimization and distributed computing expertise

使用场景

Designing and validating A/B tests for product feature rollouts
Building and deploying scalable predictive models using PyTorch or Scikit-learn
Architecting real-time inference systems with monitoring for model drift