About this role
Security Data Analyst
Location: Tel Aviv, Israel
Description
- We’re growing and looking to hire an Security Data Analyst Who embodies our core values: People First, Customer Obsession, Strive for Excellence, and Integrity.
About the Company
Claroty is a global leader in cyber-physical systems (CPS) protection, dedicated to securing the critical infrastructure that keeps the world running. We’re a fast-growing, award-winning team where innovation meets purpose—and we want you to help us define the future of cybersecurity.
At Claroty, our technology strategy is focused on solving real-world cyber-physical security problems at scale through robust, distributed, cloud-native architectures. We design and evolve a unified platform that serves as the technological backbone for all Claroty products, delivering deep visibility across CPS environments—without disrupting critical operations. Our architecture emphasizes scalability, resilience, and extensibility to support both cloud and on-prem deployments.
About the Role
Most companies give their customers an AI assistant—at Claroty, we are building an autonomous AI analyst. As part of the Claire Data team, you will conduct high-impact cybersecurity research across millions of assets, vulnerabilities, and alerts, turning complex data into actionable security insights and encoding your expert human analytical methodology into autonomous, production-grade AI agents.
Requirements
As an Security Data Analyst your impact will be:
- Research customers’ data for insights. Analyze large, complex security datasets (assets, vulnerabilities, alerts, network activity) to answer real customer questions and find insights worth acting on. Use Advanced AI tools and automated workflows to clean, normalize, and manipulate data at scale.
- Turn manual research into AI research. For each kind of question, define what data the research needs, the steps an expert would follow, and what a good answer looks like. Then encode that into agentic capabilities through prompts, tool descriptions, skills and the context the agents can draw on.
- Teach and evaluate. Build golden datasets and scoring rubrics for each research type, and measure whether the AI’s end-to-end research matches expert conclusions.
- Find and fix the gaps. Investigate where the agent falls short, using traces, evaluation infrastructure, and user feedback. Find the root cause, drive the fix, and prove it’s fixed, without breaking anything else..
- Take it from insight to action. Help decide when the agent’s crew should go beyond answering and act: open a ticket, flag a risky asset, propose a fix, with or without human review.
- Make the numbers talk. Establish KPIs and translate data trends into clear narratives and insights through dashboards and reporting, showing progress, driving action, and highlighting areas for improvement.
- Help us lead. Bring in best practices for AI-driven analytics and agent evaluation, and help make our team the authority on AI analysts in cybersecurity.