Security Data Analyst

Описание

Claroty is building an autonomous AI analyst as part of the Claire Data team. This role involves conducting high-impact cybersecurity research across millions of assets, vulnerabilities, and alerts, turning complex data into actionable security insights, and encoding expert human analytical methodology into autonomous, production-grade AI agents.

Responsibilities

• 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.

Requirements

• 3+ years of experience in data analytics or security research, with a record of turning big, messy data into insights and decisions.
• Strong SQL and Python, and comfort working at scale with tools like Spark or Databricks.
• Hands-on experience with LLMs (Large Language Models), prompt engineering, or building AI agents.