senior-data-scientist

A/B test design requires structured experiment planning, sample size calculation, and statistical modeling to measure impact. This skill guides experiment design, feature engineering, and evaluation to ensure your A/B test produces valid, data-driven results.

Causal inference identifies the true effect of a marketing campaign by applying statistical modeling to observational data. This skill performs causal analysis using Python and R to isolate campaign impact from confounding variables, driving accurate data-driven decisions.

Building a predictive churn model involves feature engineering, model training, and model evaluation using Scikit-learn and Pandas. This skill provides advanced machine learning capabilities to build and validate production-grade predictive models for business intelligence.

Python and R are fully supported for advanced statistical modeling and time series analysis. This skill leverages NumPy, Pandas, Scikit-learn, and R to perform sophisticated data analysis, build predictive models, and generate business insights.

Production ML model evaluation requires rigorous statistical methods, model evaluation metrics, and stakeholder communication. This skill handles the end-to-end process of building, evaluating, and maintaining machine learning systems for scalable data-driven decision-making.