Excellent statistics with deep, hands-on command of experimentation methods, including sample size and power calculations (MDE), variance reduction (e.g., CUPED), sequential and Bayesian approaches, and correcting for common pitfalls such as sample ratio mismatch, novelty effects, and multiple testing. Comfortable choosing the right parametric or non-parametric test, working with ratio metrics, and applying causal inference methods such as diff-in-diff, propensity score matching, regression discontinuity, and synthetic controls.