Project Controls Data Analytics & AI Tools

Application Close Date

Applications will be accepted on an ongoing basis until the requisition is closed.

At Blue Origin, we envision millions of people living and working in space for the benefit of Earth. We’re working to develop reusable, safe, and low-cost space vehicles and systems within a culture of safety, collaboration, and inclusion. Join our team of problem solvers as we add new chapters to the history of spaceflight!

This role is part of Blue Origin Operations, which is comprised of Integrated Supply Chain, Test Operations, Safety, Quality, and Mission Assurance. This includes Manufacturing and Supply Chain support across all Blue Origin facilities.

As part of a hardworking team of specialists, technicians, and engineers you will connect the systems that hold the project's truth: schedules, cost and labor actuals, procurement, change requests, engineering configuration and risk. You will turn them into one trusted controls dataset. On top of it you will build the dashboards, forecasts and AI agents that remove manual status work. Success means each PE carries more projects, with earlier warning of slips and overruns, and leadership sees the portfolio without asking anyone for a slide.

This is a builder role. You will own the controls data model and the tools built on it: data pipelines, dashboards, forecasting and risk models, and AI agents. You don't need an engineering background. You do need to learn the engineering data (design releases, BOMs, engineering changes) well enough to model it, working alongside PEs and technical leads. You will ship working tools that PEs use.

Responsibilities Include But Are Not Limited To

Requirements and architecture of the controls toolchain

• Gather PE, finance and leadership needs and turn them into technical requirements for each data product and tool.
• Map which system is the source of truth for each data element across PLM (Windchill), ERP and MRP, change tools (Jira, ITS, Change Orchestrator), scheduling (P6) and the analytics platform. Document the data flows between them.
• Test each tool against its requirements before release. Keep code, data models and AI agents under version control.
• Link design releases, engineering changes, schedule activities, cost and risk so each project can be traced through its gate reviews (SRR, IDR, FDR, Pre-Activation Review).

Forecasting, risk and simulation models

• Build and run Monte Carlo schedule and cost risk models on resource-loaded schedules, to set confidence levels for gate reviews and baselines.
• Estimate the schedule, cost and resource impact of proposed changes, so the PE can make the call with data.
• Build estimating models from completed GSD projects for hours, cost and duration.

Controls data foundation

• Build and maintain data pipelines that join schedule (Primavera P6 / IMS), cost and labor actuals, procurement, change requests, engineering configuration (PLM) and the risk register into a governed controls dataset, mostly in Databricks.
• Define the controls data model: project, WBS, control account, work package, change, risk. Own its data dictionary and quality checks.
• Find and fix data-quality gaps at the source. Missing baselines, unmapped charge codes and stale schedule status are common examples.

Analytics and reporting

• Build and run portfolio and project dashboards: schedule health (DCMA-style checks), cost and EVM metrics (CPI, SPI, EAC/VAC), change throughput, ri...

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