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)
- Strong foundation 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