About the role
Analyze large‑scale, heterogeneous security event logs spanning endpoints, identities, cloud services, and networks. Develop and train novel machine learning and statistical models for intrusion detection, anomaly detection, and adversarial behavior discovery. Design and prototype scalable data processing and analytics platforms capable of operating on high‑volume, high‑velocity security data, with a focus on low‑latency detection. Explore and apply advanced techniques such as graph‑based modeling, streaming computation, and representation learning to improve detection accuracy and timeliness. Collaborate closely with other researchers, product teams, and engineering partners to transition research ideas into practical systems with real‑world impact. Doctorate in relevant field OR Master's Degree in relevant field AND 3+ years related research experience OR Bachelor's Degree in relevant field AND 4+ years related research experience OR equivalent experience. A PhD (or equivalent experience) in computer science or a related field, with a strong research background in security, machine learning, data mining, systems, or a closely related area. Experience working with large‑scale data, including log analysis, streaming data, or distributed data processing systems. Demonstrated foundations in machine learning or statistical modeling, and interest in applying these techniques to real‑world security problems. The ability to independently drive research projects from problem formulation through implementation, evaluation, and dissemination. A collaborative mindset and interest in bridging foundational research and deployed security systems.
