Agent skill · research science · borghei
senior-data-scientist
senior-data-scientist — an agent skill for coding agents.
Why this skill is useful
Adds 4 executable scripts for A/B testing, feature engineering, statistical analysis, and model evaluation that provide capabilities not commonly found in standard data science toolkits.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Senior Data Scientist Expert data science for statistical modeling, experimentation, ML deployment, and data-driven decision making — A/B test design and analysis, feature engineering, model training/evaluation, production deployment, and causal inference. Keywords data-science, machine-learning, statistics, a-b-testing, causal-inference, feature-engineering, mlops, experiment-design, model-deployment, python, scikit-learn, pytorch, tensorflow, spark, airflow Core Capabilities Experiment design & analysis — hypothesis framing, power analysis and sample sizing, randomization, SRM monitoring, and post-hoc significance testing. Feature engineering — profiling, candidate generation (temporal/aggregation/interaction/text), selection (variance, correlation, SHAP/RFE), and leakage validation. Model training & evaluation — stratified/temporal splits, baselines, hyperparameter tuning, cross-validation, calibration, and fairness checks. Production deployment — containerized serving, input/output drift monitoring (KS/PSI), canary rollouts, and latency/error SLAs. Causal inference — propensity score matching, difference-in-differences, regression discontinuity, instrumental variables, and assumption/placebo testing. When to Use Designing or analyzing an A/B test. Building a feature engineering pipeline. Training, evaluating, or deploying an ML model. Estimating treatment effects from observational data. Clarify First Before running an analysis or pipeline, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] Task — A/B test design / feature engineering / model evaluation / causal inference (selects the script and workflow) [ ] Dataset & target variable — what you are modeling or measuring (drives feature generation and leakage validation) [ ] Decision metric & minimum effect — the metric and the smallest effect worth detecting (drives power analysis and sample size) Stop rule: ask only the 2-3 that most change the output. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/senior-data-scientist