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.
67
60%
Does it follow best practices?
Impact
68%
1.41xAverage score across 3 eval scenarios
Passed
No findings from the security scan
Fix and improve this skill with Tessl
tessl review fix ./bundled/skills/senior-data-scientist/SKILL.mdExperiment design with provided scripts
Uses experiment_designer script
0%
100%
Correct script flags
0%
100%
Power analysis script has type hints
100%
100%
Alpha = 0.05 used
100%
100%
Power = 0.80 used
100%
100%
Monitoring plan present
100%
100%
MLflow or W&B mentioned
0%
0%
Uptime/error rate target
0%
0%
Latency SLO referenced
0%
100%
Scikit-learn or statsmodels used
0%
0%
Batch processing mentioned
0%
0%
Feature engineering pipeline with reliability patterns
Uses feature pipeline script
0%
0%
Correct script flags
0%
0%
Type hints in pipeline
100%
100%
Batch processing design
75%
100%
Retry logic present
0%
0%
Circuit breaker or failure design
42%
57%
Data quality validation
100%
100%
Comprehensive tests written
100%
100%
Pandas or NumPy used
100%
100%
Comprehensive logging
100%
100%
10x scalability noted
0%
0%
Feature catalog complete
100%
100%
Model evaluation with security and monitoring
Uses model eval script
0%
0%
Correct script flags
0%
0%
PII anonymization addressed
100%
100%
Data encryption addressed
100%
100%
GDPR/CCPA compliance
100%
100%
Latency SLOs specified
100%
100%
Error rate target specified
100%
100%
MLflow or W&B for tracking
62%
100%
Canary or feature flag deployment
100%
100%
Type hints in eval script
100%
100%
Comprehensive logging in code
0%
100%
SSN/PII not logged raw
100%
100%
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