Senior Applied Scientist, Perimeter Protection Applied Science

Key job responsibilities

• Develop and implement advanced AI/ML models and algorithms to enhance the capabilities of our security services, enabling proactive threat detection, mitigation, and protection against evolving cyber threats.
• Collaborate with security engineers and researchers, to understand business/domain requirements, analyze data patterns, and translate insights into actionable solutions.
• Design and drive implementation of data pipelines and ETL processes to ingest, process, and analyze large-scale security data from multiple sources, ensuring data quality and integrity.
• Conduct in-depth data analysis, feature engineering, and model evaluation to continuously improve the performance and accuracy of AI/ML-based security solutions.
• Participate in the development and deployment of AI/ML models into production environments, ensuring scalability, reliability, and performance at cloud scale.
• Collaborate with cross-functional teams to ensure seamless integration of AI/ML solutions with our security services and infrastructure.
• Contribute to the development of best practices, documentation, and knowledge-sharing within the team and the broader organization.
• Engage in research and exploration of emerging technologies and techniques relevant to AI/ML-based security solutions, stay up-to-date with the latest trends cybersecurity, and incorporate new techniques and methodologies into our security offerings.

Basic Qualifications

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• Experience in building machine learning models for business application

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.
• Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
• Experience in applied research
• Experience with popular deep learning frameworks such as MxNet and Tensor Flow.
• Experience using managed ML/AI solutions