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Developed an incrementality estimation and bidding approach using experimental lift data and an ensemble of conversion models, iteratively validating and tuning via backend tests to achieve a 10X lift in estimated incremental conversions.
2022-2025- Built a custom analysis workflow for omni-shopping backend experiments, enabling statistically rigorous evaluation of offline conversion lift at scale, supporting 4-8 experiments per month, and saving significant analysis time while accelerating model improvements.
- Unblocked statistically significant experimentation for an offline model by correcting measurement issues, expanding eligible traffic (~2.5X), and designing a fallback holdout approach, ultimately enabling the first valid results and supporting general rollout.
Advised on statistical methodology and developed software to quickly run AB testing and reporting, resulting in an 80% reduction in process time. Reporting included visualizations for drill down decision-making. Created results repository for long-term trend analysis.
2016-2018Identified key user-level covariates and built tooling to compute them, improving the rigor and interpretability of A/B test impact analyses.
2021-2022