Section Lead, Modeling & Analytics for ISR/BMC2

Johns Hopkins Applied Physics Laboratory; Laurel (MD); Hybrid; USD 105,000 - 275,000; Full time

Benefits offered by this job

Retirement contributions

Education assistance

Generous benefits and work-life fit

Job summary

The Johns Hopkins Applied Physics Laboratory (APL) seeks a dynamic Modeling and Analytics Section Supervisor (KAT-01) to lead a hybrid team of engineers and analysts in Laurel, MD, and Colorado. You will influence ISR/T and BMC2 concepts, oversee modeling and simulation work, and drive end-to-end kill-chain evaluation in joint environments.

Ideal candidates have a strong leadership background, deep modeling expertise, and the ability to mentor staff while collaborating across groups.

Qualifications

• Bachelor’s degree in engineering, applied mathematics, computer science, physics or related field.
• At least five years in modeling, simulation and analysis, with leadership experience.
• Proficient in MATLAB and Python for analysis on Linux systems.
• Technical expertise in sensor/data fusion, RF physics, EO/IR sensors, networks, statistics, optimization, tracking.
• Domain knowledge of ISR processes, kill chain concepts and targeting/mission planning.
• Strong interpersonal and written communication skills; capable mentor.

Responsibilities

• Provide technical leadership across the section’s activities to plan and execute work.
• Use mission-level tools (AFSIM/CLCSIM) to evaluate ISR/T and BMC2 concepts and sensors.
• Promote excellence in modeling, simulation and analysis outputs.

Skills

Leadership

Mentoring

Coaching

Communication

Teamwork

Multi-tasking

Education

Bachelor's degree in engineering, applied mathematics, computer science, or physics

Tools

MATLAB

Python

Linux

Job description

The Johns Hopkins Applied Physics Laboratory (APL) seeks a dynamic Modeling and Analytics Section Supervisor (KAT-01) to lead a hybrid team of engineers and analysts in Laurel, MD, and Colorado. You will influence ISR/T and BMC2 concepts, oversee modeling and simulation work, and drive end-to-end kill-chain evaluation in joint environments.

Ideal candidates have a strong leadership background, deep modeling expertise, and the ability to mentor staff while collaborating across groups.