Teams at Fetch

Leads company-wide measurement and causal strategy, defining canonical metrics, semantic data architecture, experimentation standards, and strategic models such as marketing mix, elasticity, incentive sensitivity, and forecasting. Partners with engineering and analytics teams to build scalable, production-ready analytical frameworks, improve scientific rigor, and guide executive decisions. Provides organization-wide technical leadership, develops modeling standards, and applies advanced statistical and causal methods to high-impact business problems.

Own the scientific and product roadmap for Fetch’s monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead methodology, evaluate evidence, balance reach, precision, revenue, and cost, and convert bespoke studies into scalable products. Partner with Product, Engineering, Sales, and GTM on pilots, positioning, and customer value. Remain hands-on with SQL, Python, statistical modeling, and causal inference while mentoring scientists and influencing senior stakeholders.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.