Data Scientist (Mission Engineering)

Description du poste

You will own statistical methodology for the Mission Engineering team at CHAOS You’ll design experimental constructs that extract meaningful signals from broad trade studies and computationally expensive simulation runs, build the analytical pipelines the team relies on, push the methodological state of the art on how we characterize uncertainty, build surrogate models, and communicate quantitative results to decision-makers You will work shoulder-to-shoulder with engineers and experts in every domain to ensure that simulated, experimental, and tactical results presented by CHAOS are rigorous, reproducible, and actually deliver answers that our teams, customers, and partners need This is a foundational hire You will have the freedom to move fast and set the standards for how CHAOS does quantitative analysis from day one Design rigorous experimental constructs (DOE, space-filling designs, adaptive sampling, sequential experimentation) for large-scale simulation campaigns, getting maximum signal per simulation hour across operationally relevant trade spaces Apply advanced statistical methods (such as regression modeling, Bayesian inference, surrogate/metamodeling, sensitivity analysis, uncertainty quantification, and beyond) to simulation output to produce decision-quality conclusions Build and own scalable Python-based data pipelines for ingestion, processing, statistical analysis, and visualization of large simulation datasets Develop ML and statistical surrogate models that accelerate analysis, enable real-time trade studies, and feed mission planning applications Set team standards for data management, reproducibility, and statistical rigor (such as code review, methodology validation, and documentation practices) Translate operational and engineering questions into well-structured analytical approaches alongside M&S engineers, threat SMEs, and program staff. Push back when the framing is wrong Author technical reports and briefing materials with clear,

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