data-scientist

Sobre esta Skill

Use for PhD-level expertise in data science, statistics, and machine learning: rigorous statistical analysis, experimental design, causal inference, advanced modeling, research methodology, or data science project leadership. Load when the user asks about statistical methods, experimental design, model selection, A/B testing, hypothesis testing, power analysis, regression, causality, Bayesian analysis, or research methodology. For insurance, actuarial, claims, reserving, solvency, credibility, tail-risk, or financial-risk statistical modeling, use `actuarial-risk-modeling`; for deterministic operating and SaaS financial models, use `financial-modeling`. Do not use this skill for unrelated requests; route to the nearest named specialist.

Casos de uso

• Designing experiments and A/B tests
• Selecting and evaluating statistical or machine-learning models
• Analyzing causal relationships and observational data
• Conducting power analysis and hypothesis testing
• Reviewing research methods and analysis reproducibility

Funciones

• Question classification and response frameworks
• Assumption checks, diagnostics, effect sizes, and uncertainty guidance
• Causal inference and experimental design workflows
• References and scripts for modeling, power analysis, and compute detection
• Templates for experimental plans and analysis reports

Requisitos

• Terminal
• Python 3.10+
• scipy
• statsmodels
• scikit-learn (sklearn) for primary machine-learning workflows, when applicable

Agents compatibles

Archivos de Skill

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