senior-data-scientist
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
npx skills add majiayu000/claude-skill-registry --skill senior-data-scientist-congdon1207-agents-md --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Senior Data Scientist World-class senior data scientist skill for production-grade AI/ML/Data systems. ## Quick Start ### Main Capabilities ```bash # Core Tool 1 python scripts/experiment_designer.py --input data/ --output results/ # Core Tool 2 python scripts/feature_engineering_pipeline.py --input data/ --output features/ --config config.yaml # Core Tool 3 python scripts/model_evaluation_suite.py --input features/ --output evaluation/ --config config.yaml ``` ## Core Expertise This skill covers world-class capabilities in: - Advanced production patterns and architectures - Scalable system design and implementation - Performance optimization at scale - MLOps and DataOps best practices - Real-time processing and inference - Distributed computing frameworks - Model deployment and monitoring - Security and compliance - Cost optimization - Team leadership and mentoring ## Tech Stack **Languages:** Python, SQL, R, Scala, Go **ML Frameworks:** PyTorch, TensorFlow, Scikit-learn, XGBoost **Data Tools:** Spark, Airflow, dbt, Kafka, Databricks **LLM Frameworks:** LangChain, LlamaIndex, DSPy **Deployment:** Docker, Kubernetes, AWS/GCP/Azure **Monitoring:** MLflow, Weights & Biases, Prometh
- Quick Start
- Main Capabilities
- Core Expertise
- Tech Stack
- Reference Documentation
- 1. Statistical Methods Advanced
- 2. Experiment Design Frameworks
- 3. Feature Engineering Patterns
- Production Patterns
- Pattern 1: Scalable Data Processing
- Pattern 2: ML Model Deployment
- Pattern 3: Real-Time Inference
- Best Practices
- Development
Core Tool 1 python scripts/experiment_designer.py --input data/ --output results/ Core Tool 2 python scripts/feature_engineering_pipeline.py --input data/ --output features/ --config config.yaml Core Tool 3 python scripts/model_evaluation_suite.py --input features/ --output evaluation/ --config config.yaml Experiment design python scripts/experiment_designer.py --input data/ --output results/ --verbose Feature engineering Model evaluation
What does the senior-data-scientist skill do?
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill senior-data-scientist-congdon1207-agents-md --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.