Summary
What you’ll impact
Our organization is seeking a Principal Applied Scientist to lead the development, validation, and application of advanced computational models and simulations for Ubiquitous Technical Surveillance (UTS) environments. The role involves creating high-fidelity agent-based models, integrating machine-learning features, and delivering a transition-ready risk-management capability to improve mission survivability for joint forces.
Responsibilities
What you'll do
- Serve as the scientific lead for computational modeling and simulation activities across the program.
- Establish scientifically rigorous methodologies for representing UTS phenomena within analytical and simulation environments.
- Ensure analytical approaches accurately reflect the underlying physical, operational, and statistical characteristics of the data.
- Design, develop, and validate complex agent-based models representing UTS interactions and operational environments.
- Develop agent behaviors, environmental conditions, interaction rules, and emergent system dynamics
- Evaluate simulation outputs for realism, repeatability, and operational relevance.
- Evaluate government-provided datasets to identify meaningful physical, temporal, behavioral, and operational relationships.
- Develop scientifically valid approaches for feature engineering and diagnostic vector identification.
- Collaborate with data scientists to ensure machine learning models leverage meaningful scientific features rather than spurious correlations.
- Assess the quality, completeness, and limitations of available data.
- Apply statistical, probabilistic, and computational methods to characterize uncertainty and system behavior.
Requirements
What you’ll bring
- US Citizen and must have an active TS/SCI clearance, with ability to obtain CI Poly.
- 12+ years of relevant experience.
- Master's or PhD in physics, mathematics, statistics, operations research, engineering, science, or related discipline.
- Strong technical leadership experience.
- Extensive demonstrated knowledge and experience utilizing the following modeling techniques: process, predictive, physics-based, agent-based.
- Significant current experience (i.e., within last 2 years) in modeling, using statistics to conduct predictive modeling and make estimates on future occurrences based on prior events, and using physics-based modeling to review sensor capabilities and predict sensor ability to perform tasks in a synthetic scenario
- Experience working with diverse data sets from UTS relevant sources
- Proficiency in Python and scientific computing libraries.
- Experience AI/ML programs.
- Ability to clearly communicate technical concepts and analytical results in writing and verbally.
- Must be able to work on-site in Herndon, Va.
- Experience with agent-based modeling.
- Experience supporting operational environments and/or Government R&D projects.
- published modeling reports and/or authored new procedures/methods to create innovative modeling approaches to solve complex issues
- Experience modeling human behavior, adversarial systems, or complex adaptive systems.
- Experience with reinforcement learning, Bayesian inference, Monte Carlo methods, or stochastic simulation.
- Familiarity with explainable AI (XAI) and integrating scientific principles into machine learning workflows.
- Experience developing digital twins or high-fidelity operational simulations.
- Experience supporting personnel recovery, force protection, intelligence analysis, or mission planning.