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Weapon System Analyst

The Johns Hopkins University Applied Physics Laboratory
life insurance, paid time off
United States, Maryland, Laurel
11100 Johns Hopkins Road (Show on map)
Jul 31, 2026
Description

Do you have a passion for physics, math, and data analysis?

Do you enjoy taking a complex physical problem and trying to represent it through mathematics and code?

Are you looking for a role where you can be mentored by world-class experts and grow into a specialist in physics-based modeling and statistical inference?

Are you interested in defining methodologies and processes that will be used for the testing and evaluation of current and future weapon systems?

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

The System Modeling, Evaluation, and Planning Group is seeking experienced analysts to support the technical evaluation of the nation's primary strategic deterrents. You will join a multidisciplinary team of engineers, physicists, and mathematicians.

As a Weapon System Analyst you will...

  • Develop and Refine Physics Models by creating and improve high-fidelity physics-based models of complex systems, including inertial navigation (accelerometers, gyroscopes) and missile/reentry dynamics.
  • Lead the experimental design process for flight and ground tests to ensure data collection is optimized for model validation and parameter estimation.
  • Perform Statistical Validation by leveraging real-world collected data to estimate underlying physics-based errors, using statistical methods to determine how well models predict actual system behavior.
  • Apply advanced statistical techniques to "work backward" from observed data to identify the physical properties or errors that caused the observed effect.
  • Build Computational Frameworks by developing robust tools in C++ , MATLAB, or Python to facilitate analyses for important reports and deliverables.
  • Assess the accuracy of current strategic weapon systems and support the engineering of future systems to meet mission needs.

Qualifications

You meet our minimum requirements if you...

  • Hold a Bachelor's degree in Physics, Applied Math, Applied Statistics, Engineering, or a closely related field.
  • Have one or more years of professional experience in data analysis or physics-based modeling
  • Have strong foundational skills in a scientific language (C++, MATLAB, or Python).
  • A solid grasp of linear algebra, calculus, and basic probability/statistics.
  • A strong desire to learn complex physical systems and a willingness to dive deep into technical documentation
  • Are able to acquire an Interim Secret level security clearance by your start date and can ultimately acquire a final Top Secret level 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'll exceed our minimum requirements with experience in any of the following...

  • Statistical analysis, parameter estimation and inverse problems, Kalman filtering, experimental design, Bayesian inference (e.g., MCMC), or Maximum Likelihood Estimation
  • Have experience working in large collaborative environments that require teams of engineers, physicists, statisticians, and applied mathematicians
  • Prior experience with tactical or strategic missile systems, or prior experience with inertial navigation systems
  • Hold an active Secret or higher-level clearance

About Us

Why Work at APL?

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.

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 athttps://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 contactAccessibility@jhuapl.edu.

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
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