senior-data-scientist
davila7/claude-code-templates
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.
What is senior-data-scientist?
A comprehensive senior data scientist skill covering production-grade statistical modeling, experiment design, feature engineering, and ML deployment. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions in enterprise environments.
- Advanced statistical methods and causal inference for rigorous analysis
- Experiment design frameworks and A/B testing infrastructure
- Feature engineering pipelines and model evaluation suites
- Production ML deployment with monitoring and drift detection
- Scalable data processing with distributed computing frameworks
- Real-time inference systems with latency optimization
How to install senior-data-scientist
npx skills add https://github.com/davila7/claude-code-templates --skill senior-data-scientist- Python with NumPy, Pandas, Scikit-learn installed
- SQL database access for data queries
- Docker and Kubernetes for deployment (optional but recommended)
- Git for version control
- Familiarity with statistical methods and ML concepts
How to use senior-data-scientist
- 1.Run experiment_designer.py to set up A/B test frameworks and hypothesis validation
- 2.Execute feature_engineering_pipeline.py to build and transform features for modeling
- 3.Use model_evaluation_suite.py to evaluate, compare, and validate model performance
- 4.Deploy models using Docker and Kubernetes following the production patterns provided
- 5.Set up monitoring with MLflow or Weights & Biases to track model drift and performance
- 6.Review reference documentation for statistical methods, experiment design, and feature engineering patterns
Use cases
- Designing and running A/B tests to validate product hypotheses
- Building and deploying predictive models for business decisions
- Performing causal analysis to understand treatment effects
- Optimizing ML model performance and reducing inference latency
- Setting up monitoring and automated retraining for production models
- Senior data scientists and ML engineers
- Data science team leads and technical managers
- Analytics engineers building production data systems
- Researchers implementing causal inference studies
- Cross-functional teams driving data-driven decisions
senior-data-scientist FAQ
Python (primary), SQL, R, Scala, and Go. The skill emphasizes Python with libraries like NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow.
Yes. It includes production ML deployment patterns, model serving infrastructure, A/B testing setup, feature store integration, and monitoring with MLflow or Weights & Biases.
Yes. The skill includes comprehensive experiment design frameworks, A/B testing infrastructure, statistical validation methods, and causal inference techniques.
Spark, Airflow, dbt, Kafka, Databricks, BigQuery, Snowflake, PostgreSQL, and Pinecone for data processing, orchestration, and storage.
Yes. The skill includes senior-level responsibilities covering technical leadership, mentoring junior engineers, establishing best practices, and driving architectural decisions.
Full instructions (SKILL.md)
Source of truth, from davila7/claude-code-templates.
name: senior-data-scientist description: 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.
Senior Data Scientist
World-class senior data scientist skill for production-grade AI/ML/Data systems.
Quick Start
Main Capabilities
# Core Tool 1
python scripts/experiment_designer.py --input data/ --output results/
# Core Tool 2
python scripts/feature_engineering_pipeline.py --target project/ --analyze
# Core Tool 3
python scripts/model_evaluation_suite.py --config config.yaml --deploy
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, Prometheus Databases: PostgreSQL, BigQuery, Snowflake, Pinecone
Reference Documentation
1. Statistical Methods Advanced
Comprehensive guide available in references/statistical_methods_advanced.md covering:
- Advanced patterns and best practices
- Production implementation strategies
- Performance optimization techniques
- Scalability considerations
- Security and compliance
- Real-world case studies
2. Experiment Design Frameworks
Complete workflow documentation in references/experiment_design_frameworks.md including:
- Step-by-step processes
- Architecture design patterns
- Tool integration guides
- Performance tuning strategies
- Troubleshooting procedures
3. Feature Engineering Patterns
Technical reference guide in references/feature_engineering_patterns.md with:
- System design principles
- Implementation examples
- Configuration best practices
- Deployment strategies
- Monitoring and observability
Production Patterns
Pattern 1: Scalable Data Processing
Enterprise-scale data processing with distributed computing:
- Horizontal scaling architecture
- Fault-tolerant design
- Real-time and batch processing
- Data quality validation
- Performance monitoring
Pattern 2: ML Model Deployment
Production ML system with high availability:
- Model serving with low latency
- A/B testing infrastructure
- Feature store integration
- Model monitoring and drift detection
- Automated retraining pipelines
Pattern 3: Real-Time Inference
High-throughput inference system:
- Batching and caching strategies
- Load balancing
- Auto-scaling
- Latency optimization
- Cost optimization
Best Practices
Development
- Test-driven development
- Code reviews and pair programming
- Documentation as code
- Version control everything
- Continuous integration
Production
- Monitor everything critical
- Automate deployments
- Feature flags for releases
- Canary deployments
- Comprehensive logging
Team Leadership
- Mentor junior engineers
- Drive technical decisions
- Establish coding standards
- Foster learning culture
- Cross-functional collaboration
Performance Targets
Latency:
- P50: < 50ms
- P95: < 100ms
- P99: < 200ms
Throughput:
- Requests/second: > 1000
- Concurrent users: > 10,000
Availability:
- Uptime: 99.9%
- Error rate: < 0.1%
Security & Compliance
- Authentication & authorization
- Data encryption (at rest & in transit)
- PII handling and anonymization
- GDPR/CCPA compliance
- Regular security audits
- Vulnerability management
Common Commands
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/
# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth
# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/
# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py
Resources
- Advanced Patterns:
references/statistical_methods_advanced.md - Implementation Guide:
references/experiment_design_frameworks.md - Technical Reference:
references/feature_engineering_patterns.md - Automation Scripts:
scripts/directory
Senior-Level Responsibilities
As a world-class senior professional:
-
Technical Leadership
- Drive architectural decisions
- Mentor team members
- Establish best practices
- Ensure code quality
-
Strategic Thinking
- Align with business goals
- Evaluate trade-offs
- Plan for scale
- Manage technical debt
-
Collaboration
- Work across teams
- Communicate effectively
- Build consensus
- Share knowledge
-
Innovation
- Stay current with research
- Experiment with new approaches
- Contribute to community
- Drive continuous improvement
-
Production Excellence
- Ensure high availability
- Monitor proactively
- Optimize performance
- Respond to incidents
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