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senior-data-scientist skill

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This skill provides production-grade data science capabilities for experimentation, modeling, deployment, and decision-making with scalable, explainable

This is most likely a fork of the senior-data-scientist skill from openclaw

npx playbooks add skill benchflow-ai/skillsbench --skill senior-data-scientist

Review the files below or copy the command above to add this skill to your agents.

Files (7)

SKILL.md

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---
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

```bash
# 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

```bash
# 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:

1. **Technical Leadership**
   - Drive architectural decisions
   - Mentor team members
   - Establish best practices
   - Ensure code quality

2. **Strategic Thinking**
   - Align with business goals
   - Evaluate trade-offs
   - Plan for scale
   - Manage technical debt

3. **Collaboration**
   - Work across teams
   - Communicate effectively
   - Build consensus
   - Share knowledge

4. **Innovation**
   - Stay current with research
   - Experiment with new approaches
   - Contribute to community
   - Drive continuous improvement

5. **Production Excellence**
   - Ensure high availability
   - Monitor proactively
   - Optimize performance
   - Respond to incidents

Overview

This skill provides a world-class senior data scientist capability for statistical modeling, experimentation, causal inference, and advanced analytics. It bundles expertise across Python, R, SQL, scalable architectures, and MLOps to deliver production-ready workflows. Use it to design experiments, build predictive models, perform causal analysis, and translate results into business decisions.

How this skill works

The skill inspects data sources, experiment definitions, and model artifacts to recommend designs, features, metrics, and evaluation suites. It produces reproducible pipelines for feature engineering, training, validation, deployment, and monitoring, and it outlines production patterns for scalability, low-latency serving, and drift detection. Outputs include experiment plans, model evaluation reports, and deployment checklists tailored to your tech stack.

When to use it

Designing randomized experiments or A/B tests with power calculations and analysis plans
Building and validating predictive models for business KPIs or operational automation
Performing causal inference to estimate treatment effects from observational data
Setting up production ML systems: feature stores, CI/CD, monitoring, and retraining
Optimizing time series forecasting, anomaly detection, or real-time inference pipelines

Best practices

Define clear success metrics and pre-specify analysis plans before running experiments
Use test-driven development and version control for models, data schemas, and pipelines
Instrument model performance and data quality with automated monitoring and alerts
Adopt feature stores and reproducible feature engineering to prevent training/serving skew
Automate deployments with canaries/feature flags and maintain rollback plans

Example use cases

Design an A/B test with stratified randomization, sample size calculation, and post-hoc analysis
Build an end-to-end forecasting pipeline with feature engineering, backtesting, and deployment
Estimate causal impact of a marketing campaign using matching, difference-in-differences, or synthetic controls
Productionize a classification model with low-latency serving, drift detection, and automated retraining
Create a live monitoring dashboard that tracks P50/P95/P99 latency, throughput, and error rates

FAQ

Which languages and frameworks are supported?

Primary support for Python (NumPy, Pandas, Scikit-learn, PyTorch, XGBoost), R, SQL, and common data platforms like Spark, Airflow, and dbt.

Can it help with deployment and monitoring?

Yes — it provides patterns for containerized deployment, Kubernetes, model serving, observability, and automated retraining pipelines.