Senior Security Research Engineer, Elastic

• If you are passionate about security research as well as data science and would enjoy the challenge of mixing your security expertise with your analytics driven from data science, we would love to have you join our growing team!
• Experience building Security solutions (Endpoint, Network, UEBA, etc.)
• Experience with threat hunting and malware analysis
• Proficiency in Python programming skills
• Experience with writing and running tests (unit tests, integration tests, regression tests)
• Experience with performing data analysis as required to support data quality decisions
• Ability to both give and receive helpful code reviews
• Be comfortable working in a fully-remote environment
• Be able to communicate clearly to diverse groups of stakeholders coming from different disciplines, time zones, and programming language preferences
• Willingness to learn new things and ask for help

Desirable

• Elastic Security focuses on AI driven Security Solutions across our SIEM and Endpoint products
• The Security ML team researches, designs, and builds AI and ML solutions and drives innovation in this domain
• We are looking for a Senior Security Engineer with Data Science experience to join our ML team and continue to innovate and build new models that will help secure our users against the latest emerging threats
• You will collaborate with the broader Elastic Security team, which consists of a diverse group of skilled Researchers, ML Engineers, Data Scientists and Developers who possess extensive domain expertise in their respective areas
• Our geographically dispersed team values positivity and inclusion in the workplace, collaborative learning and candid communication
• Contribute to developing ML solutions for Endpoint Security
• Perform data analysis on our sources to identify new solutions and improvements
• Collaborate within the Security ML team and with members of other teams to build, ship, and maintain ML models
• Promote long-term vision for monitoring performance of deployed models to identify concept drift and determine retraining cadence
• Determine how to improve models over time by leveraging implicit and explicit feedback