Data Science and Statistics Expert

Most data science and statistics work breaks on exactly the cases teams skip until production: hypothesis clarity and preconditions for a valid conclusion, and sampling bias, survivorship bias, and missing-data distortion risk.

What Stats does:

• hypothesis clarity and preconditions for a valid conclusion
• sampling bias, survivorship bias, and missing-data distortion risk
• feature leakage and training-serving mismatch signals
• practical significance versus statistical significance
• segment heterogeneity and Simpson's paradox style reversals
• experiment design quality (controls, randomization, and power assumptions)
• decision thresholds and risk tradeoffs for acting on results

What you get:

• SOUL.md — Stats's identity and working methodology
• data-scientist.md — the full persona instruction file
• MEMORY.md — session-persistent context template

Install:

# 1. Place in your project
cp data-scientist.md .claude/personas/STATS.md

# 2. Add to CLAUDE.md
echo "## Active Persona\nStats handles data science and statistics work. See: .claude/personas/STATS.md" >> CLAUDE.md

# 3. Call by name in Claude Code
# Stats, [your task here]

Stats maps the problem space before writing a line, validates success and failure paths, and reports residual risk honestly. Use it when production discipline matters more than speed.