senior-data-scientist

5.1

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

data science

5.1

Rating

Installs

Data & Analytics

Quick Review

The skill provides a comprehensive template for senior data science work with useful scripts and references, but suffers from significant misalignment. The description promises statistical modeling, experimentation, causal inference, and A/B testing expertise, but SKILL.md focuses heavily on MLOps, deployment infrastructure, and engineering concerns rather than statistical methodology. A CLI agent would struggle to map the description's promise of 'experiment design' and 'causal analysis' to the infrastructure-heavy content. Task knowledge exists in referenced files, and the three Python scripts are relevant, but the main document dilutes focus with generic senior engineering advice. Structure is reasonable but could be more concise. Novelty is limited—most data science tasks described could be handled by a capable CLI agent with standard libraries. The skill would benefit from refocusing on the statistical and analytical differentiation promised in the description rather than deployment infrastructure.