domains / families / data / data-scientist

data-scientist

combined

Data science methodology for Python research: EDA, validation, causal inference (IV, DiD, RD, synthetic control), clustering/PCA/UMAP, supervised ML, geospatial, visualization. Method selection guidance. For syntax, load tool-specific skills. Expert data scientist for advanced analytics, machine learning, and

When to use

• Working on data scientist tasks or workflows
• Needing guidance, best practices, or checklists for data scientist
• Expert data scientist combining strong statistical foundations with modern machine learning techniques and business acumen. Masters the complete data science workflow from exploratory data analysis to production model deployment, with deep…

When not to use

• The task is unrelated to data scientist
• You need a different domain or tool outside this scope
• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The first two copies are near-identical role prompts that list broad capabilities (statistical methods, ML, visualization, business analytics) but provide no concrete procedure. The third copy is a distinct, rigorous methodology skill that defines non-negotiable principles (data robustness, documentation, verification, code documentation, research focus) and specific workflow constraints (file-first execution, loading tool-specific skills). Because the third copy contains material steps and constraints the others lack, and the first two are generic role definitions, crowning one would hide the real alternative of a procedural methodology skill.