Senior Data Scientist
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics.
Before & after
Same prompt, same agent — the only difference is the skill.
A/B testing is a great way to validate ideas before rolling them out. You'll want to define a clear hypothesis about what change might improve conversion. You should think about how to randomly split your users into test and control groups. It's important to have a large enough sample size so your results are statistically significant. You might want to look at multiple metrics, not just conversion, to make sure you're not hurting other parts of the experience. You could run the test for a week or two to gather enough data. Then you'll analyze the results using statistical methods to see if there's a real difference. Would you like help defining your hypothesis or choosing metrics?
A/B TEST DESIGN: Subscription Conversion HYPOTHESIS Changing pricing display from annual to monthly (with annual option) will increase conversion without reducing LTV. SAMPLE SIZE CALCULATION Baseline conversion: 2.8% Minimum detectable effect: 0.4 percentage points (15% relative lift) Statistical power: 90% Significance: 0.05 (two-tailed) Sample per arm: 18,400 users Total required: 36,800 users Expected duration: 12 days TEST SETUP Control: Annual pricing, 49,000/year, savings emphasis Treatment: Monthly option first (4,099/month), annual in fine print Randomization: User ID hash to treatment, stratified by cohort ANALYSIS PLAN Primary: Two-proportion z-test (alpha 0.05) Secondary: Kaplan-Meier curves for 90-day retention (Logrank test) Monitored: Revenue per convert, CAC, support tickets Sensitivity: Bonferroni-corrected for secondaries (alpha 0.025) FAILURE CRITERIA Treatment LTV-12m lower than control: Do not roll out Churn spike month 2: Revert and investigate Lift below 10%: Extend to 21 days (underpowered) INTERPRETATION GUIDE 0-5% lift: Implement if change cost is low 5-15% lift: Implement with monitoring 15%+ lift: Fast-track to full rollout
About this skill
name: senior-data-scientist description: Use when World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics.
Senior Data Scientist
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.
What you get
- Public GitHub repo (alirezarezvani/claude-skills)
- the senior-data-scientist skill folder with SKILL.md. Part of a 337-skill / 30-agent / 70-command install.
Customize your output
- Fork the repo and adapt the skill's instructions and references to your workflow.
Example output
Activates automatically when your request matches Senior Data Scientist; chains with the other skills, agents, and commands in the collection.
Best for
Creators, builders, and teams using Claude Code.
SKILL.md preview
---
name: senior-data-scientist
description: Use this skill when designing or analyzing controlled experiments, building and evaluating classification or regression models, performing causal analysis, or translating statistical findings into business decisions.
version: 1.0.0
category: Development / Engineering
author: AgentVolt
license: proprietary
tags:
- development
- engineering
---
# Senior Data Scientist
Applies rigorous statistical modeling, experiment design, and causal inference to real data problems, using Python, R, and SQL, rather than surface-level metric reporting.
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