Director, GTM On-Demand Capacity Planning

Nscale•Houston, NY

About The Position

You are the commercial architect of our physical supply chain. Balancing long-lead and massive GPU deployments with explosive customer demand requires flawless yield management. You are a player-coach who will build the exact mathematical models dictating how we allocate our compute power. This is not a traditional software forecasting role; you are dealing with rigid physical hardware constraints. You will dive deeply into the ambiguity of our deployment schedules, build the foundational capacity allocation frameworks, and ensure we never leave a cluster unmonetized while strictly protecting our SLAs for existing enterprise commitments.

Requirements

7+ years of rigorous experience in capacity planning, supply chain finance, or cloud operations at a hyperscaler, heavy-infrastructure tech firm, or logistics-heavy enterprise.
Exceptional analytical rigor and an advanced mastery of data modeling.
You are incredibly comfortable digesting massive datasets regarding power costs, hardware depreciation, and network utilization.
High agency and an innate ability to independently solve complex yield management problems under immense ambiguity.
The ability to thrive in a high-pressure environment, pushing back on sales leaders when capacity simply does not exist, while maintaining a collaborative, solution-oriented relationship.
Deep understanding of the macro AI compute market and how utilization rates directly affect corporate valuation and cash flow.

Responsibilities

Design and manage the core capacity allocation frameworks, expertly balancing highly volatile on-demand spot instances with long-term, high-margin reserved customer commits.
Partner intimately with Nscale Engineering and Supply Chain to forecast exact cluster availability, and translate those physical deployment dates into viable, sellable inventory for the sales floor.
Build dynamic financial models that track the utilization of every megawatt. You will identify under-utilized compute pools and proactively partner with Deal Strategy to incentivize the sales team to move specific capacity.
Act as the ultimate arbiter of capacity allocation when customer demand outstrips our physical supply, using high EQ and rigorous data to prioritize deployments based on strategic value and margin.
Build models to account for hardware deprecation cycles, ensuring our pricing and capacity allocation strategies maximize ROI over the lifecycle of the GPU clusters.