# Fixed Asset Manager

**Company:** [Fluidstack](https://hotfix.jobs/companies/fluidstack)
**Location:** Austin, TX, New York, NY, San Francisco, CA, Seattle, WA
**Role:** Data Analytics
**Salary:** $109k – $128k/yr
**Skills:** forecasting models, demand planning, lead-time analysis, supplier capacity tracking, SQL, Python, statistical forecasting, machine learning forecasting, data reconciliation, dashboard development
**Posted:** 2026-07-17

> Build and maintain demand, lead-time, and capacity forecasts for critical data center equipment to keep multi-GW AI compute build pipelines on schedule. Partner with procurement using data-driven models and dashboards to mitigate risks and optimize multi-hundred-million-dollar commitments.

## Job Description

## Role Scope
- Build and maintain the demand and lead-time forecasts that keep procurement ahead of the forward build pipeline across every critical equipment category, from chillers to switchgear to transformers to generators.
- Track supplier capacity, lead times, and delivery risk across dual-sourced categories, and surface the thresholds that trigger a re-source before the schedule slips.
- Model the cost and timing trade-offs on multi-hundred-million-dollar equipment commitments so buyers negotiate from data, not guesswork.
- Turn raw supplier, shipment, and inventory data into the weekly signal the supply chain team runs on, reconciling sources that don't agree.
- Stand up the forecasting tooling and dashboards that let a lean team see disruption coming instead of reacting to it.

## What We're Looking For
- You've built forecasting or planning models that people actually made decisions on, not slideware that got ignored.
- You've worked with messy operational data from several systems and turned it into a number a team trusted.
- You catch a lead-time or capacity problem in the data weeks before it becomes a schedule problem.
- You write analysis clearly enough that a buyer or an executive can act on it without a follow-up meeting.
- You've partnered with procurement, operations, or finance and shipped analysis that changed what they bought or when.
- You check your own numbers and you know when a forecast is guessing rather than telling you something.
- **Bonus:** Supply chain, procurement, or logistics analytics. Data center or hardware equipment categories. SQL and Python for data work. Statistical or machine-learning forecasting methods. Scaling analysis inside a high-growth operation.

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