senior-data-scientist

To calculate A/B test sample size, you provide the baseline conversion rate and minimum detectable effect. This Skill processes these inputs to determine the exact statistical sample size needed for a rigorous experiment design.

Difference-in-differences is a causal inference technique used to understand treatment effects from observational data. This Skill applies statistical modeling to isolate the causal impact by comparing changes in treatment and control groups over time.

Yes, you can build feature engineering pipelines using Scikit-learn and evaluate models with MLflow. This Skill supports transforming raw data into features and logging machine learning model training and cross-validation results.

Yes, this data science workflow supports analysis with Python, R, and SQL. It integrates these languages to provide advanced statistical modeling, feature engineering, and predictive modeling capabilities in production environments.

To analyze A/B test results, you apply statistical methods to compare experiment outcomes against baselines. This Skill evaluates the data to determine if a new feature had a statistically significant impact on user behavior.

You need causal inference when understanding the direct effect of an action is required, rather than just predicting outcomes. This Skill applies techniques like difference-in-differences to extract true causal effects from observational data.