Data Scientist

Armis from ServiceNow protects the entire attack surface and manages an organization's cyber risk exposure in real time. Combined with ServiceNow's Security and Risk capabilities, as well as Identity Security capabilities from Veza, we bring together all critical capabilities to secure every asset, every identity - incl. AI Agents - across all environments.

As an AI-driven cybersecurity company, Machine Learning and Generative AI power the core capabilities that make Armis an industry leader. Our algorithms are the engine behind our advanced Vulnerability Management, Detection, and Remediation (VMDR) platform.

• End-to-End Ownership: Drive complex AI and ML initiatives through the full product lifecycle from early-stage prototyping and deep domain analysis to production-grade deployment and monitoring.
• Architect & Deploy: Design and build production-ready AI models and system pipelines in Python using modern cloud and big data infrastructure (AWS, Spark).
• LLM Innovation: Integrate state-of-the-art Generative AI and LLM workflows directly into our core VMDR features to drive automated detection, prioritization, and remediation insights.
• Domain Deep-Dives: Partner closely with domain researchers, data engineers, and product leaders to dive into granular cybersecurity data and translate complex security problems into high-impact AI solutions.

• Cybersecurity Background: Domain experience in cybersecurity (specifically Vulnerability Management, Asset Intelligence, or Threat Detection) is a big advantage, but not mandatory.
• LLM Expertise: At least 2 years of hands-on experience working with Large Language Models (LLMs), including prompt engineering, fine-tuning, RAG, agentic workflows, and orchestration frameworks (e.g., LangChain, LlamaIndex, vLLM, or similar).
• Solid ML Foundations: Deep algorithmic and theoretical knowledge in Machine Learning, supported by an Academic Degree (M.Sc. or Ph.D. preferred) or equivalent research experience.
• Production Engineering Skills: 5+ years of software development experience, with strong, production-grade Python coding abilities and experience deploying ML systems at scale.
• Self-Driven & Collaborative: An autodidact who naturally takes full ownership of problem domains, stays ahead of emerging AI trends, and thrives in a high-trust, collaborative team environment.