Senior Data Scientist

Built for data analysis workflows where you need statistical rigor and modeling expertise. It handles exploratory data analysis, feature engineering, model selection, and evaluation with a focus on production readiness. The skill brings knowledge of classical ML, deep learning frameworks, and experimental design to help you make defensible modeling decisions. Useful when you're past the CSV munging stage and need to actually validate assumptions, tune hyperparameters, or explain why your model generalizes. It won't replace domain expertise, but it'll push you toward better statistical practices and catch common pitfalls in your pipeline.

skills.sh installs: 975
GitHub stars: 29.5K
last updated: Aug 21, 2026

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Install

$ npx -y skills add alirezarezvani/claude-skills --skill senior-data-scientist --agent claude-code

Installs into .claude/skills of the current project.

Files

Senior Data Scientist

World-class senior data scientist skill for production-grade AI/ML/Data systems.

Core Workflows

1. Design an A/B Test

import numpy as np
from scipy import stats

def calculate_sample_size(baseline_rate, mde, alpha=0.05, power=0.8):
    """
    Calculate required sample size per variant.
    baseline_rate: current conversion rate (e.g. 0.10)
    mde: minimum detectable effect (relative, e.g. 0.05 = 5% lift)
    """
    p1 = baseline_rate
    p2 = baseline_rate * (1 + mde)
    effect_size = abs(p2 - p1) / np.sqrt((p1 * (1 - p1) + p2 * (1 - p2)) / 2)
    z_alpha = stats.norm.ppf(1 - alpha / 2)
    z_beta = stats.norm.ppf(power)
    n = ((z_alpha + z_beta) / effect_size) ** 2
    return int(np.ceil(n))

SKILL.md on GitHub

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