About the job
Join Armis, a pioneering force in cyber exposure management and security, dedicated to safeguarding organizations against cyber threats. In today's dynamic, perimeter-less environment, Armis ensures that businesses consistently detect, protect, and manage their critical assets, spanning from ground operations to cloud infrastructures. We proudly serve Fortune 100, 200, and 500 companies, along with national and local governments, to enhance the security of vital infrastructure, economies, and societies around the clock.
Headquartered in California, Armis is a privately held company committed to innovation.
As a Senior Data Scientist at Armis, you will play a crucial role in powering our industry-leading Exposure Management (EM) platform. Your expertise will drive the AI models behind our Cyber Asset Attack Surface Management (CAASM) features and advanced Vulnerability Prioritization and Remediation systems.
You will contribute to the development of an intelligence layer that enables organizations to implement a Continuous Threat Exposure Management (CTEM) framework. This involves building models that normalize fragmented data from diverse sources, compute intricate risk scores, and prioritize potential threats to the business.
We seek an experienced and innovative data scientist who possesses a strong affinity for data and a passion for software development to join our Data Science team.
Key Responsibilities:- Tackle Product Challenges: Dive deep into the Armis product ecosystem to identify and address significant challenges. Your work will translate complex customer needs into scalable machine learning features that enhance user experience.
- Lead Exposure Intelligence R&D: Develop features that integrate asset data (CAASM) with threat intelligence, including creating models for entity resolution and automated risk assessment.
- Advanced NLP & Knowledge Extraction: Employ NLP and LLMs to analyze unstructured security data—such as CVEs, threat intel feeds, and security advisories—to automate vulnerability mapping in specific business contexts.
- Predictive Prioritization: Design and optimize algorithms that surpass traditional capabilities.