Data Scientist - Cato Networks

Minimum 3 years of professional experience in Data Science roles Hands-on experience in networking and/or cybersecurity domains Proven experience building and deploying LLM-based applications and agents (e.g., RAG pipelines, tool-use agents, prompt engineering at scale) Stron g fou ndation in classical machine learning methods (supervised, unsupervised learning, clustering) Practical experience with deep learning frameworks such as TensorFlow or PyTorch, including NLP techniques Proven experience deploying models on cloud platforms like AWS, Azure, or Google Cloud Excellent analytical, problem-solving, and communication skills Self-motivated, collaborative, and able to work independently

Lead the design, development, and deployment of AI/ML models that enhance Cato’s security and networking products Leading networking and security research, including analysis of large-scale network traffic and security data to identify patterns, threats, and opportunities for product improvement Research, fine-tune, and train models optimized for real-time inline inference under limited compute resources Collaborate cross-functionally with product, engineering, and support teams to translate product and business needs into AI solutions Define and track success metrics to ensure AI solutions meet performance and business goals

Minimum 3 years of professional experience in Data Science roles Hands-on experience in networking and/or cybersecurity domains Proven experience building and deploying LLM-based applications and agents (e.g., RAG pipelines, tool-use agents, prompt engineering at scale) Stron g fou ndation in classical machine learning methods (supervised, unsupervised learning, clustering) Practical experience with deep learning frameworks such as TensorFlow or PyTorch, including NLP techniques Proven experience deploying models on cloud platforms like AWS, Azure, or Google Cloud Excellent analytical, problem-solving, and communication skills Self-motivated, collaborative, and able to work independently