Data Scientist

Cato Networks is the converged network and security cloud for the AI era.

As AI reshapes cybersecurity, enterprises face a dual challenge: unlocking AI’s potential while defending against AI-driven attacks. Cato addresses both through a unified, cloud-native platform, providing the visibility and context to prioritize risks and stop threats in real time across users, applications, data, and AI agents.

Join us as we scale: take on ambitious technological challenges and shape the next generation of enterprise security at a company trusted by thousands of organizations worldwide.

Data Scientist position

We are looking for a talented and experienced Data Scientist to join our Research Data Science team and play a key role in shaping the future of cloud-native network security . Your primary mission will be to lead the development and deployment of AI-driven capabilities that protect enterprise networks at scale through Cato’s SASE platform , powering core products such as our AI assitant, DLP, XDR, IPS , and more. Leveraging rich, real-time data from our global backbone and Cloud data warehouse , you will apply advanced reserach analytics, machine learning, deep learning and GenAI techniques to solve complex cybersecurity and networking challenges.

This is an exciting opportunity to join a fast-growing company and drive innovation in the rapidly evolving SASE space.

Key Responsibilities

• 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

Qualifications

• 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
• How to Stand Out
• Experience with real-time or low-latency AI inference in production environments.
• Advanced degree (MSc or PhD) in Computer Science, Statistics, Mathematics, or a related quantitative field