Data Scientist: Mission Engineering

CHAOS Industries is redefining modern defense with a multi-product portfolio that gives the ultimate advantage—domain dominance. The company's products are powered by Coherent Distributed Networks (CDN™), empowering warfighters, commercial air operators, and border protection teams to act faster, adapt rapidly, and stay ahead of evolving threats.  CHAOS Industries was founded in 2022 and has raised a total of $1 billion in funding from leading investors, including 8VC, Accel, and Valor Equity Partners. The company is headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. For more information, please visit www.chaosinc.com. About the Team: Mission Engineering at CHAOS turns simulation output into decisions. We run large-scale modeling and simulation campaigns across all warfighting domains and the full kill chain, against named threats, in operationally relevant scenarios, at the speed engineering, operational, and customer teams actually need. Every CHAOS engineering trade, pursuit, and customer engagement is anchored in rigorous, physics-based, tactically relevant, and statistically valid analysis, and we're scaling the function to meet that bar across a growing product portfolio. About the Role: 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. What You'll Do: 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, me...