senior-data-scientist

Guides statistical modeling, experiment design, and causal inference workflows in Python and R.

Updated Aug 28, 2026

Search Tags:#python #data science #machine learning #feature engineering #statistical modeling #causal inference #a/b testing

Ready to run

npx skills add https://github.com/listentomi/Orcajack --skill senior-data-scientist-listentomi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: senior-data-scientist
Source: https://github.com/listentomi/Orcajack/tree/main/skills/science/senior-data-scientist
Command: npx skills add https://github.com/listentomi/Orcajack --skill senior-data-scientist-listentomi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? It provides structured senior-level guidance for data science work, covering experiment design, feature engineering, model evaluation, and production ML deployment so teams avoid ad-hoc analysis and unreliable models. ## Core Features & Use Cases - Experiment Design: Plan A/B tests and causal inference studies using documented frameworks in the references directory. - Feature Engineering & Modeling: Apply feature engineering patterns and model evaluation practices with Python, Scikit-learn, and SQL. - Production ML Guidance: Follow patterns for model deployment, monitoring, drift detection, and MLOps. - Use Case: When designing an A/B test for a new product feature, use this Skill to structure the hypothesis, choose statistical methods, and define evaluation metrics before writing code. ## Quick Start Ask the AI to design an A/B test plan with sample size estimation and evaluation metrics for a new checkout flow.

Frequently Asked Questions about senior-data-scientist

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design an A/B test with proper statistical methods?▼

A/B test design involves defining a hypothesis, choosing primary metrics, estimating sample size, and selecting significance thresholds before running the experiment. The references/experiment_design_frameworks.md document covers step-by-step processes and troubleshooting procedures.

What tools does this skill use for feature engineering?▼

Feature engineering guidance centers on Python with Pandas, NumPy, and Scikit-learn, plus SQL for data extraction. The references/feature_engineering_patterns.md file documents implementation examples and configuration best practices.

Can I use this skill with R instead of Python?▼

Yes, the skill covers both Python and R for statistical methods, along with SQL for data access. Python is the primary language for the referenced scripts and ML frameworks like Scikit-learn and XGBoost.

Does this skill cover model deployment and monitoring?▼

Yes, it documents production ML patterns including model serving, A/B testing infrastructure, drift detection, and automated retraining. Monitoring tools referenced include MLflow, Weights & Biases, and Prometheus.

When is causal inference preferred over standard A/B testing?▼

Causal inference methods apply when randomized experiments are impractical, such as observational data with confounding variables. The statistical methods reference covers advanced techniques for these scenarios alongside standard experimental design.