We are seeking a Principal PMT for the Publisher Decision Engine - Ad Server team to own and improve Programmatic Guaranteed deal delivery, which is a key offering from Amazon Ads that enables publishers to monetize their inventory.
PubTech (Publisher Technology) builds and operates extensible services that empower publishers (like Prime Video, Twitch, Tubi, and Paramount+) to improve the monetization of their customer experiences, along with the experiences themselves. PubTech offers various deal products that enable publishers to package inventory and sell it to advertisers. A key deal offering is the Programmatic Guaranteed (PG) deal, which allows publishers to sell an impression guarantee to advertisers for a given flight duration and price. PG deals are expected to deliver 100% of the impressions promised for all deals. Not meeting PG deal delivery targets could result in advertisers redistributing their budgets, which impacts revenue for both Amazon and the respective publishers.
Key job responsibilities
The Amazon Ads and PubTech Ad Server team owns and operates multiple algorithms that work together to optimize Programmatic Guaranteed (PG) deal delivery. These algorithms incorporate PG deal forecasts to determine how to pace deals toward their impression and spend targets, set appropriate impression goals, and select which PG deal(s) to serve for each incoming bid request.
As a PM of PG deal deliver in the Ad server team, your key responsibilities will include:
Creating a roadmap to improve current PG Deal Delivery Rates across our advertisers.
Design current algorithm inputs and outputs and leverage controller theory to improve PG deal delivery.
Define requirements for algorithms that account for seasonality, trends, advertiser delivery expectations (even, front loaded, etc), revenue implications and others .
Design experiments and iterate to improve on PG Deal Delivery.
A day in the life
Your morning will begin with analyzing Programmatic Guaranteed deal delivery metrics, identifying underperforming campaigns and diagnosing root causes across the pacing algorithms, delivery planner targets, and ad selection logic. As you identify root causes and systemic issues, you will collaborate with TPMs on the delivery program to align stakeholder expectations and troubleshoot critical delivery gaps. The afternoon involves cross-functional design reviews with engineering teams and applied science teams across delivery planner, pacing algorithms, deal selection, ad ranking, and bid request optimization systems. You will define requirements to iterate on the pacing algorithm implementation, ensuring it accounts for traffic seasonality and advertiser delivery preferences, while simultaneously working on new features like dynamic daily goal adjustments and improved PG deal prioritization logic. Your day closes with data deep-dives using analytics tools to measure the impact of recent algorithm changes on delivery rates and revenue, preparing recommendations for the next sprint"s roadmap to drive the team closer to the delivery target for standard activation PG deals.
About the team
You"ll join a group of product managers, applied scientists, and engineers Amazon"s advertising ecosystem focused on improving PG deal delivery across. Our team owns the Publisher Ad server that owns forecasting total available impressions for a PG deal, Delivery planner that provides how many impressions a deal has to deliver to hit final impression target, deal selection and ad selection that selects the ad (that could be PG) for a given ad. These systems and algorithms enable Programmatic Guaranteed product delivery, helping advertisers secure inventory commitments with confidence while maximizing publisher yield. We work at the intersection of large-scale data engineering, statistical modeling, and product strategy. The team collaborates closely with yield optimization, sales, and publisher-facing teams to ensure our forecasts drive critical business decisions. We value technical depth, data-driven decision making, and the ability to translate complex analytical problems into practical product solutions.