Applied Scientist, Leo Security

About the Role

Amazon Leo is a constellation of Low Earth Orbit satellites providing low-latency, high-speed broadband network connectivity. We are seeking an Applied Scientist to join the founding cohort of the Engineering and R&D team within Leo Space Security. This team's mission is to defend the manufacturing lines, launch sites, and global ground infrastructure of the constellation from sophisticated threat actors. The data involved is unique, including badge and door-access events, asset movement, network telemetry, and industrial control signals. You will develop statistical and behavioral models to differentiate threat actor behavior from operational noise, and create privacy-preserving data representations for scalable detection science without compromising sensitive data. This R&D role carries a production mandate, with models directly integrated into the systems used by Leo's security teams.

Key Responsibilities

• Build behavioral and statistical models to baseline normal activity across heterogeneous security telemetry, including specialized industrial-control and factory-floor data sources, to achieve low false-positive rates in new environments.
• Design privacy-preserving representations of sensitive security data and verify model accuracy against these representations, ensuring data-protection guarantees are measurable and provable.
• Define methodologies and lead analysis for complex, ill-defined problems by gathering cross-domain data, selecting appropriate data science methods, and justifying approaches with evidence.
• Develop metrics and evaluation frameworks to measure model and detection performance against threat actor behavior before production deployment.
• Contribute to the team's neurosymbolic reasoning platform, adapting state-of-the-art techniques and delivering production-quality components.
• Document work with the rigor of a peer-reviewed publication and communicate findings clearly to both scientific and security-operations audiences.

Requirements & Tech Stack

• 3+ years of experience building models for business application.
• PhD, or Master's degree and 4+ years of experience in Computer Science (CS), Computer Engineering (CE), Machine Learning (ML), or a related field.
• Experience with statistical modeling and analysis of large, heterogeneous datasets, including anomaly detection, time-series analysis, or behavioral baselining.
• Fluency in Python.
• Experience with popular deep learning frameworks (e.g., PyTorch, TensorFlow).
• Experience with a systems language such as Rust or C++ is a plus.

Qualifications

• Experience in one or more of the following domains: access-control systems and methodology, network security, application- and system-development security, security architecture and models, cryptography, and operations security.
• Experience in professional software and systems development.
• Publications at top-tier peer-reviewed machine learning, data mining, or security venues (e.g., NeurIPS, ICML, ICLR, KDD, CCS, USENIX Security).
• Experience with privacy-preserving machine learning, including synthetic data generation, anonymization, or differential privacy, and validating the fidelity of transformed data.
• Experience designing evaluation frameworks and metrics for models where ground truth requires human judgment.
• Experience modeling operational or sensor telemetry, including industrial control systems (OT/SCADA), IoT, or physical-world event data.
• Candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum due to applicable export control laws and regulations.