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senior-data-scientist

Designs experiments, builds predictive models, and performs statistical analysis with Python, R, and SQL.

Updated Jul 7, 2026
Search Tags:#data science#machine learning#experiment design#feature engineering#statistical modeling#causal inference#a/b testing
One-click install
npx skills add https://github.com/singhaganesh/Urban-assist --skill senior-data-scientist-singhaganesh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: senior-data-scientist
Source: https://github.com/singhaganesh/Urban-assist/tree/main/.cursor/skills/senior-data-scientist
Command: npx skills add https://github.com/singhaganesh/Urban-assist --skill senior-data-scientist-singhaganesh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Data science work often stalls on experiment design, feature engineering, and model evaluation decisions that require senior-level statistical judgment. This Skill provides structured guidance for statistical modeling, A/B testing, causal inference, and production ML workflows so teams can make defensible data-driven decisions. ## Core Features & Use Cases - Experiment Design: Frameworks for A/B testing, hypothesis formulation, and statistical power analysis documented in the references directory. - Feature Engineering & Modeling: Patterns for building features and evaluating models with Scikit-learn, XGBoost, PyTorch, and TensorFlow. - Production ML Guidance: Covers model deployment, monitoring, drift detection, and MLOps practices with MLflow, Docker, and Kubernetes. - Use Case: A product team wants to validate a new pricing page. Use this Skill to design the A/B test, compute required sample sizes, analyze results with proper statistical methods, and document the decision for stakeholders. ## Quick Start Ask the assistant to design an A/B test for your feature, including hypothesis, sample size calculation, and an analysis plan using the experiment design frameworks.

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 power?▼

Define a clear hypothesis and primary metric, then compute the required sample size from your baseline rate, minimum detectable effect, significance level, and power. The experiment design frameworks reference walks through this process step by step.

What Python libraries are used for statistical modeling and machine learning?▼

The skill centers on NumPy, Pandas, and Scikit-learn for core analysis and modeling, with XGBoost for gradient boosting and PyTorch or TensorFlow for deep learning. MLflow and Weights & Biases cover experiment tracking.

Can this skill help with causal inference beyond A/B testing?▼

Yes, the description explicitly covers causal inference and causal analysis for cases where randomized experiments are not feasible. It supports observational study reasoning alongside standard experimentation workflows.

Does it cover model deployment and monitoring in production?▼

Yes, it documents production ML patterns including model serving, A/B testing infrastructure, drift detection, and automated retraining. Deployment guidance references Docker, Kubernetes, and cloud platforms.

What are the limitations of the reference documentation?▼

The three reference files are high-level framework overviews rather than executable code or detailed mathematical derivations. They provide principles and patterns, so you still need to implement the actual analysis in your own environment.

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