Project Controls Data Analytics & AI Tools

BLUE ORIGIN•West Melbourne, FL

1d•$130,706 - $182,988

About The Position

Blue Origin Operations is seeking a builder to develop and maintain the controls data model and the tools built on it. The role connects project schedules, cost and labor actuals, procurement, change requests, engineering configuration, and risk into a trusted controls dataset. It uses that foundation to create dashboards, forecasts, risk models, and AI agents that reduce manual status work, provide earlier warning of schedule slips and cost overruns, and give leadership portfolio visibility. The role does not require an engineering background, but it does require learning engineering data such as design releases, bills of materials, and engineering changes while working with project engineers and technical leads. The tools are expected to be practical, tested, and used by project engineers. The position owns requirements and architecture for the controls toolchain; forecasting, risk, and simulation models; the governed controls data foundation; analytics and reporting; AI tools and automation; and adoption standards. It works with project engineers, Systems Engineering, finance, procurement, site teams, and enterprise IT and data teams. AI tools must operate within ITAR and data-handling requirements, use approved internal platforms, preserve human approval for baseline changes, log sources for generated numbers, and be evaluated for measurable value. The role requires a bachelor's degree in a quantitative or technical field, or an equivalent combination of education and directly relevant experience, and at least five years in project controls, business intelligence, or data analytics, including at least two years building data products or tools for project, construction, engineering, or finance teams. Strong SQL and Python, production data pipeline experience, data modeling, BI tools, project-controls knowledge, hands-on AI/LLM tooling, Git, and the ability to explain data and model limitations are required. Applicants must meet the stated U.S. citizenship, permanent-residency, refugee, or asylum status requirements. The listed base pay range for Washington applicants is $130,706.00–$182,987.70; ranges at other sites may differ.

Requirements

Bachelor's degree in a quantitative or technical field, such as data science, statistics, information systems, computer science, mathematics, economics, finance, business analytics, construction management, or engineering; an equivalent combination of education and directly relevant experience is also accepted.
At least 5 years of experience in project controls, business intelligence, or data analytics.
At least 2 years building data products or tools for project, construction, engineering, or finance teams.
Experience translating business problems into technical requirements and delivering a dashboard or data product used regularly by a team for decision-making.
Strong SQL and Python skills.
Experience building production ETL/ELT data pipelines, rather than only one-off analyses.
Data modeling experience, including star schemas, snapshots, and slowly changing dimensions.
Experience with a BI tool such as Power BI or Tableau.
Working knowledge of project controls, including CPM scheduling, baselines, change control, cost metrics, and EVM metrics.
Hands-on experience building with LLMs or AI tools, including prompting, retrieval, tool or agent workflows, and evaluation of outputs.
Experience with version control, such as Git.
Ability to explain data and model limitations clearly to engineers and leaders.
Must be a U.S. citizen or national, a U.S. permanent resident with a current Green Card, or lawfully admitted into the U.S. as a refugee or granted asylum.

Nice To Haves

Master's degree in analytics, data science, statistics, or a related field.
Experience with Databricks or Spark and cloud data platforms such as AWS or Azure.
Experience with Primavera P6 or MS Project data structures and EVMS (EIA-748) reporting.
Experience with engineering or PLM data, such as Windchill, bills of materials, or engineering changes, or experience pulling ERP and Jira data through APIs.
Experience with Monte Carlo schedule risk analysis, time-series forecasting, or discrete event simulation.
Capital construction, launch, test, or heavy-industrial project experience.
Experience building an AI agent or automation that eliminated a recurring manual task, with a measured result.
Software engineering practices for data work, including code review, automated tests, and CI/CD.
PMI-SP, AACE (PSP or EVP), PMP, or a similar certification; certification is not required.

Responsibilities

Gather project engineer, finance, and leadership needs and translate them into technical requirements for data products and tools.
Identify and document systems of record and data flows across PLM, ERP/MRP, change-management tools, scheduling systems, and the analytics platform.
Test tools against requirements before release and keep code, data models, and AI agents under version control.
Connect design releases, engineering changes, schedule activities, cost, and risk so projects can be traced through gate reviews.
Build and run Monte Carlo schedule and cost risk models using resource-loaded schedules to establish confidence levels for gate reviews and baselines.
Estimate schedule, cost, and resource impacts of proposed changes.
Build estimating models using completed GSD projects to estimate hours, cost, and duration.
Build and maintain pipelines joining schedule, cost and labor actuals, procurement, change requests, engineering configuration, and risk-register data into a governed controls dataset, primarily in Databricks.
Define and maintain the controls data model for projects, WBS, control accounts, work packages, changes, and risks, including its data dictionary and quality checks.
Identify and correct data-quality gaps at their source, including missing baselines, unmapped charge codes, and stale schedule status.
Build and run portfolio and project dashboards covering schedule health, cost and EVM metrics, change throughput, risk burn-down, and project engineer workload.
Automate weekly status and variance reviews and monthly forecast packages so project engineers can review and approve rather than compile them.
Build and calibrate forecasts for schedule slip, cost at completion, and resource demand using completed-project history.
Model project engineer and integrated product team capacity against intake demand to support accept, defer, or decline decisions.
Design, build, and deploy AI agents and LLM-based tools for project setup, schedule-to-scope checks, variance narratives, change-impact summaries, and risk identification.
Apply safeguards to AI tools, including human approval for baseline changes, source logging for generated numbers, and regression testing.
Measure AI tool outcomes in project engineer hours saved or cycle time reduced, and retire tools that do not provide sufficient value.
Comply with ITAR and data-handling rules and use only approved internal AI platforms and data boundaries.
Work with project engineers, Systems Engineering, finance, procurement, and site teams to identify and automate high-value manual work.
Write standards, runbooks, and training to help project engineers use the tools independently.
Partner with enterprise IT, data, and tool teams so GSD tools build on company platforms rather than duplicate them.

Benefits

Medical insurance
Dental insurance
Vision insurance
Basic and supplemental life insurance
Paid parental leave
Short- and long-term disability
401(k) with company match of up to 5%
Education Support Program
Stock options for regular employees working at least 20 hours per week
Paid time off of up to four weeks per year based on weekly scheduled hours
Up to 14 company-paid holidays
Some employees may be eligible for benefits and bonuses depending on role type, job level, and other factors; amounts and eligibility are not guaranteed.