Senior Manager, Demand Planning and Market Economics

Crusoe•Sunnyvale, CA

5d•$195,000 - $235,000

About The Position

Demand for AI compute is growing faster than any infrastructure market has before, with new buyers, new deployment models, and new competitors emerging every quarter. This role brings structure and quantitative rigor to that pace: sizing how much of that demand is real and addressable, recommending how to deploy against it, forecasting our ability to monetize it, and building the financial case behind the capital decisions that will shape the business for years to come. You'll turn a market that's moving faster than most models can track into a clear, defensible view the CFO and executive team can act on.

Requirements

7+ years in demand planning, supply/capacity planning, or quantitative finance, ideally in cloud infrastructure, data centers, semiconductors, or another capital-intensive, forecast-driven business
Strong financial modeling skills and comfort building a credible view of the market without perfect data.
Knowledge of statistical forecasting tools and techniques (e.g. time series, regression, scenario modeling)
Experience synthesizing external market and competitive information with internal deal-level data
Outstanding written and verbal communication, with a track record of turning numbers into narratives that influence senior decision makers
Bachelor's degree in Finance, Economics, Math, or a related analytical field

Nice To Haves

MBA or Masters in Analytics a plus
Domain expertise in AI Infrastructure
Experience with SQL and/or BigQuery

Responsibilities

Size the total addressable market for AI compute: who's buying chips, who's consuming compute, and how that's trending.
Assess whether industry-wide data center and power supply can actually support the demand that's out there, not just whether the demand exists on paper.
Build and maintain the long term forecast for AI compute and power capacity demand, based on market dynamics, product mix, signed contracts, and renewal/churn assumptions.
Track how the customer segment mix is shifting over time and what that implies for the business.
Recommend the mix of Colo, full owned builds, and Spark modular deployments against different segments and demand profiles; feed that recommendation into capital allocation and investment decisions.
Utilize signals like demand volatility, ramp timing, and supply-chain constraints to develop our canonical pricing forecast model; track forecast accuracy over time and explain the drivers behind misses.
Build the financial case for build-ahead vs. build-to-order tradeoffs at the portfolio level.
Turn the market view, deployment recommendation, and approach to scaling into material that can go to the CFO, the Board, or other executive audiences.