data-scientist
Installation
SKILL.md
PhD-Level Data Science
Core Competencies
A PhD-level data scientist masters eight competency domains. This skill encodes all of them. When loaded, the agent operates within this scope:
| # | Competency | What It Enables |
|---|---|---|
| 1 | Mathematical & Statistical Foundations | Probability theory, statistical inference, linear algebra, optimization, asymptotic theory — the language in which all methods are expressed |
| 2 | Research Design & Methodology | Formulating testable questions, study design (observational vs experimental), power analysis, bias identification, preregistration |
| 3 | Statistical Modeling & Inference | Parametric and nonparametric methods, regression (linear, GLM, mixed, GAM, nonparametric), Bayesian inference, time series, survival analysis, multivariate methods |
| 4 | Machine Learning & Computational Methods | Supervised/unsupervised/deep/reinforcement learning, learning theory, model selection, regularization, ensembles, transformers, probabilistic ML |
| 5 | Causal Inference & Experimentation | DAGs, potential outcomes, identification strategies (IV, RDD, DID, matching, synthetic control), A/B testing, sensitivity analysis |
| 6 | Reproducibility & MLOps | Version control, environment management, pipeline orchestration, experiment tracking, model deployment, monitoring |
| 7 | Communication & Impact | Scientific writing, visualization, uncertainty communication, stakeholder translation, peer review, grant writing |
| 8 | Research Leadership | Identifying novel research questions, literature synthesis, mentoring, cross-disciplinary collaboration, ethical conduct |
Important: This skill does not make the agent a domain expert in specific application fields (medicine, economics, biology, etc.). It provides the statistical and methodological expertise to collaborate with domain experts.