Manager, PUDO Pricing & Monetization

eBay is a global marketplace connecting millions of buyers and sellers across more than 190 markets. People come to eBay to discover unique and pre-loved goods, pursue their interests, and grow businesses. Our purpose is to connect people and build communities to create economic opportunity for all.

About the team and the role PUDO (pick-up and drop-off) is a major central initiative to create a materially lower-cost shipping proposition for eBay and Depop. A central question is whether lower shipping prices and other customer interventions actually change buyer and seller behavior enough to create incremental marketplace value.

This role owns the analytical evidence behind those decisions. This person will design and evaluate pricing and incentive experiments, measure elasticity and incrementality, understand how response varies across customer and inventory segments, and determine whether observed behavior supports the assumptions in the PUDO economic model.

What you will accomplish

• Design and evaluate pricing, subsidy, incentive and offer experiments that test how buyers and sellers respond to changes in the PUDO proposition
• Measure shipping-price elasticity, adoption, repeat behavior and incrementality across customer, seller, category, ASP, geography and parcel-flow segments
• Distinguish true marketplace growth from shipment substitution, promotional lift, mix shift and other effects that can make adoption appear stronger than it is
• Build cohort and causal analyses that determine whether lower shipping prices create incremental conversion, transaction frequency, GMV or addressable low-ASP demand
• Partner with the Economics leader to translate observed customer response into pricing, subsidy, break-even and scale recommendations
• Establish measurement plans before tests launch, including hypotheses, success metrics, control / comparison groups, guardrails and decision thresholds
• Diagnose performance when results differ from expectation: identify the likely causal drivers, test competing explanations and recommend the next experiment or business action
• Build reusable analytical tools and automated reporting that allow the team to evaluate pricing and adoption decisions quickly without creating a large analytics support layer

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