Space Situational Awareness Analyst

Johns Hopkins Applied Physics Laboratory•Laurel, MD

4d•Onsite

About The Position

The Space Analysis and Technologies (SAT) Group within APL's Space Exploration Sector develops advanced analytical methods, modeling and simulation capabilities, and decision-support tools that help solve some of the nation's most challenging space mission problems. Our multidisciplinary team combines expertise in applied mathematics, estimation theory, astrodynamics, autonomy, sensing, and software development to deliver innovative solutions for sponsors across the national security and civil space communities. We thrive on tackling complex, ambiguous problems, collaborating across technical disciplines, and turning rigorous analysis into mission impact. We’re looking for someone who likes turning incomplete space situational awareness problems into working models and clear answers. You’ll spend your time building simulations, testing estimation and decision methods, and helping teams understand what the results mean for real missions. If you enjoy building quantitative tools that help people make better decisions about what’s happening in space, we’d like to hear from you!

Requirements

MS or PhD in Applied Mathematics, Aerospace Engineering, Physics, Statistics, Operations Research, Electrical Engineering, Computer Science, or a related quantitative field
2+ years of professional experience building and testing mathematical models, simulations, or algorithms
Background in one or more of: estimation theory, Bayesian inference, statistical signal processing, decision theory, optimization, probability and statistics, stochastic processes, or uncertainty quantification
Proficiency with Python for analysis, modeling, simulation, and visualization
Ability to take an incomplete problem, make reasonable assumptions, run the analysis, and explain both the results and the uncertainty
Clear written and verbal communication skills
Are able to obtain TS/SCI level security clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

Nice To Haves

PhD in a relevant quantitative field
Experience applying estimation, inference, optimization, or decision analysis to space situational awareness or space domain awareness problems
Experience with Kalman filtering, nonlinear estimation, multi-target tracking, orbit determination, sensor fusion, sensor management, information theory, optimal control, or partially observable decision processes
Experience running sensitivity studies, Monte Carlo analyses, trade studies, or algorithm evaluations
Experience with C++ or MATLAB for scientific computing
Experience or interest in decision-making methods for autonomous space systems
Experience presenting analytical results through visualizations, concise writing, and sponsor briefings
Active Secret clearance or higher

Responsibilities

Design and run mathematical models and simulations for tracking, estimation, prediction, uncertainty, and decision-making problems
Write Python code to prototype algorithms, evaluate performance, and produce clear technical results
Develop analysis plans for open questions, define metrics, run sensitivity studies, and communicate findings
Test estimation and decision algorithms, validate results, quantify uncertainty, and explain operational impact
Create frameworks that support sensor management, information fusion, and mission planning
Work with experts in astrodynamics, sensing, autonomy, and mission operations
Contribute methods that improve decision support and mission planning under uncertainty
Share results through plots, briefings, short reports, and direct discussion with teams and sponsors