Architect data reconciliation frameworks to unify disparate datasets from multiple systems (e.g., resource symphony, allocation data, and fleet guidance), ensuring a single, high-fidelity source of truth for the ML fleet.
Audit and resolve data discrepancies across cross-functional team inputs to maintain the integrity of fleet capacity and usage metrics.
Develop and maintain complex investigative models that enable real-time, dynamic forecasting of ML fleet supply and demand, accounting for hardware transitions and shifting deployment timelines.
Engineer automated dashboards that provide leadership with what-if scenario modeling to visualize the impact of divestments or increased GPU demand on the total fleet.
Translate fleet capacity into actionable allocation plans for Product Areas (PAs), ensuring resource distribution aligns with Google’s ML priorities and commitments.

Technical Program Manager III, ML Strategy and Allocations, Cloud AI

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