GNSS Data Science and Navigation Analyst

Johns Hopkins Applied Physics Laboratory (APL) seeks a GNSS Data Science and Navigation Analyst to develop, analyze, and evaluate advanced navigation and timing solutions for critical national security and space missions. You will apply data science, statistical modeling, and signal processing to GNSS and alternative PNT datasets, design simulations, and assess system performance and resilience in contested environments. Collaborating with multidisciplinary experts, you will prototype algorithms, document methods, and present findings to sponsors while contributing to innovative, mission-driven research.

Responsibilities

• Apply data science and signal processing to GNSS and navigation datasets.
• Design and execute simulations to evaluate navigation and timing performance.
• Develop and prototype navigation and PNT algorithms and tools.
• Analyze system resilience in contested and degraded GNSS environments.
• Visualize and interpret complex geospatial and time-series data.
• Document methods, results, and models in clear technical reports.
• Present findings and recommendations to sponsors and internal stakeholders.
• Collaborate with cross-disciplinary teams of engineers and scientists.
• Support proposal development and research planning for new projects.

Required Skills

• GNSS signal processing
• Python or MATLAB programming
• Statistical modeling
• Machine learning for navigation
• Kalman filtering and state estimation
• Data visualization (e.g., matplotlib, Plotly)
• Simulation and modeling tools
• Geospatial analysis
• Version control (Git)
• Technical writing and briefing