senior-data-scientist
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on
Profile →npx skills add alirezarezvani/claude-skills --skill senior-data-scientist --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. ## Core Workflows ### 1. Design an A/B Test ```python import numpy as np from scipy import stats def calculate_sample_size(baseline_rate, mde, alpha=0.05, power=0.8): """ Calculate required sample size per variant. baseline_rate: current conversion rate (e.g. 0.10) mde: minimum detectable effect (relative, e.g. 0.05 = 5% lift) """ p1 = baseline_rate p2 = baseline_rate * (1 + mde) effect_size = abs(p2 - p1) / np.sqrt((p1 * (1 - p1) + p2 * (1 - p2)) / 2) z_alpha = stats.norm.ppf(1 - alpha / 2) z_beta = stats.norm.ppf(power) n = ((z_alpha + z_beta) / effect_size) ** 2 return int(np.ceil(n)) def analyze_experiment(control, treatment, alpha=0.05): """ Run two-proportion z-test and return structured results. control/treatment: dicts with 'conversions' and 'visitors'. """ p_c = control["conversions"] / control["visitors"] p_t = treatment["conversions"] / treatment["visitors"] pooled = (control["conversions"] + treatment["conversions"]) / (control["visitors"] + treatment["visitors"]) se = np.sqrt(pooled * (1 - pooled) * (1 / control["visitors"] + 1 / treatment["visitors"])) z = (p_t - p
- Core Workflows
- 1. Design an A/B Test
- 2. Build a Feature Engineering Pipeline
- 3. Train, Evaluate, and Select a Prediction Model
- 4. Causal Inference: Difference-in-Differences
- Reference Documentation
- Common Commands
Testing & linting
python -m pytest tests/ -v --cov=src/
python -m black src/ && python -m pylint src/
Bundled pipeline scaffolds (stdlib runners — extend the process() body with project logic)
python3 scripts/experiment_designer.py --input experiment_spec.json --output experiment_design.json
python3 scripts/feature_engineering_pipeline.py --input raw_features.json --output features.json
python3 scripts/model_evaluation_suite.py --input model_predictions.json --output evaluation.json
Each prints a JSON run report ({status, processed_items, start/end_time}); any status other
than "completed" means the stage failed — fix before moving to the next pipeline stage.What does the senior-data-scientist skill do?
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill senior-data-scientist --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 alirezarezvani/claude-skills, a repository with 23,791 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.