Computational Scientist, Senior

Booz Allen Hamilton•Mclean, VA

16h•Remote

About The Position

As a computational scientist, you live at the boundary between mathematics and software, building systems where the correctness of the code depends on the correctness of the underlying science. You've developed numerical methods, implemented surrogate models, or built simulation frameworks where understanding the physics matters as much as understanding the architecture. Booz Allen is building a platform that makes computational models composable, verifiable, and fast enough to drive real-time operational decisions, and we need someone like you to make the science work in production. As the scientific computing lead on this effort, you'll own the layer that underpins two integrated products. One product requires models that can be validated in isolation, checked for compatibility when composed together, and executed with traceable evidence of correctness. You'll design the validation logic, compatibility checking, and observable binding that makes composition trustworthy. The other product requires probabilistic estimation from noisy, incomplete data streams. You'll build the uncertainty modeling and state estimation that turns raw signals into a working picture of the world that a solver can reason over. In both cases, your work is the foundation that everything else depends on. You'll join a small, elite team where everyone operates at the technical frontier. You'll be the person on the team who takes a concept from a technical paper or a whiteboard and turns it into tested, production-grade code the rest of the team builds on. You'll build in a rapid development environment with AI-native tooling, delivering scientific rigor at engineering velocity. Join us. The world can't wait.

Requirements

5+ years of experience developing computational models, numerical solvers, or physics-based simulation codes in a professional or research environment
Experience implementing reduced-order modeling techniques, surrogate modeling, or data-driven approximation methods for dynamical systems
Experience defining and enforcing validity conditions for computational models, including parameter bounds, domain constraints, or interface specifications between heterogeneous codes
Knowledge of probability theory, statistical estimation, or uncertainty quantification as applied in computational systems
Ability to translate mathematical formulations from research literature into tested, maintainable production software
Ability to obtain a TS/SCI clearance with a polygraph
Bachelor's degree in an Applied Mathematics, Physics, Computational Science, Mechanical Engineering, or Aerospace Engineering field, or 8+ years of experience working in a professional environment in lieu of a degree

Nice To Haves

Experience with operator-theoretic methods such as Koopman analysis, dynamic mode decomposition, or spectral methods for nonlinear systems
Experience building model repositories, simulation frameworks, or workflow systems that manage heterogeneous computational assets
Experience building products for defense, intelligence, or national security customers
Master's degree in Applied Mathematics, Physics, Computational Science, or a related quantitative field preferred
Doctorate degree in Applied Mathematics, Physics, Computational Science, or a related quantitative field a plus

Responsibilities

Own the layer that underpins two integrated products.
Design the validation logic, compatibility checking, and observable binding that makes composition trustworthy.
Build the uncertainty modeling and state estimation that turns raw signals into a working picture of the world that a solver can reason over.
Translate mathematical formulations from research literature into tested, maintainable production software.