Design, develop, and validate robust causal inference models (e.g., synthetic control, difference-in-differences, double machine learning) to isolate the incremental impact of GCS programs. Partner with business teams to design and execute rigorous A/B tests, defining the sample sizes, power analyses, and success metrics required for valid results. Stay current with the latest academic research in causal ML and econometrics, proactively prototyping new methods to improve the precision of our impact estimates.
Distill highly technical methodologies and black box model outputs into clear, prescriptive business narratives for non-technical executive audiences. Establish comprehensive monitoring systems to track model performance, detect data drift, and ensure the ongoing accuracy of deployed measurement frameworks.
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