Space Situational Awareness Analyst

Johns Hopkins Applied Physics Lab; Laurel (MD); On-site; USD 100,000 - 245,000; Full time

Job summary

Johns Hopkins Applied Physics Laboratory (APL) is seeking a Space Situational Awareness Analyst to design and run models and simulations that address tracking, estimation, and decision-making under uncertainty. You will prototype algorithms in Python, validate results, and communicate insights to sponsors across national security and civil space communities.

The role requires 2+ years of work in quantitative modeling, strong communication skills, and the ability to obtain TS/SCI clearance.

Qualifications

• MS or PhD in a quantitative field as listed.
• 2+ years building and testing mathematical models, simulations, or algorithms.
• Background in estimation theory, Bayesian inference, or related areas.
• Proficiency with Python for analysis, modeling, simulation, and visualization.
• Ability to articulate results and uncertainty clearly.
• Must be able to obtain TS/SCI level security clearance; U.S. citizenship required.

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 technical results.
• Develop analysis plans, define metrics, run sensitivity studies, and communicate findings.
• Test estimation and decision algorithms, validate results, quantify uncertainty, and explain operational impact.
• Create frameworks to support sensor management, information fusion, and mission planning.
• Collaborate 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, reports, and sponsor discussions.

Skills

Mathematical modeling

Estimation theory

Bayesian inference

Clear communication

Problem solving under uncertainty

Security clearance readiness

Education

MS or PhD in Applied Mathematics

Aerospace Engineering

Physics

Statistics/Operations Research

Electrical Engineering/Computer Science

Tools

Python

MATLAB

C++

Job description

Description

Do you love building models that answer some of the toughest questions in space operations?

Are you energized by developing simulations, testing new algorithms, and seeing your work directly influence mission success?

Do you enjoy turning ambiguous, real-world problems into algorithms, models, and actionable insights that influence mission decisions?

If so, we're looking for someone like you to join our team at APL!

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!

As a Space Situational Awareness Analyst you will...

• 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

Qualifications

You meet our minimum qualifications for the job if you possess...

• 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.

You go above and beyond the minimum qualifications if you have...

• 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

About Us

The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation’s most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates.

Why Work at APL?

At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL’s campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at https://www.jhuapl.edu/careers.

All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact [e-mail].

The referenced pay range is based on JHU APL’s good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis.

Minimum Rate

$100,000 Annually

Maximum Rate

$245,000 Annually