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CyberCube is a leading cyber risk analytics platform trusted by 75% of top European and US cyber insurance carriers and 70% of top ten reinsurance brokers. The company uses AI and deep cybersecurity expertise to help global carriers, reinsurers, and brokers quantify, price, and manage cyber risk in financial terms.
As a Security Research Engineer (Quantitative Cyber Risk Engineer), you will work at the intersection of network engineering, cybersecurity, and data science. You will analyze how global businesses configure their internet-facing assets and design mathematical signals that quantify their exposure to cyber attacks. Your research and models will directly drive the next generation of risk-scoring and analytical engines for the cyber insurance industry.
Key responsibilities include:
- Design innovative, quantitative signals that measure a company's exposure to cyber attacks based on network architecture, asset configurations, and internet footprint
- Translate theoretical cybersecurity concepts and complex network topologies into empirical, actionable risk metrics and assumptions
- Evaluate and extract insights from how organizations deploy and configure internet-facing protocols, cloud infrastructure, and enterprise networks
- Conduct in-depth research on emerging cyber threats (vulnerabilities, ransomware, exploits, breach techniques) and map them to underlying asset configurations
- Serve as subject matter expert on internet architecture, collaborating with product, analytics, and engineering teams to integrate risk signals into production models
You will work alongside Data Scientists, Engineers, and Product teams in a global organization with offices in San Francisco, New York, London, and Tallinn. The culture emphasizes collaboration, openness, intellectual rigor, and ownership for excellence.
REQUIREMENTS:
- Deep Internet & Networking Expertise: Expert-level understanding of how the internet works at foundational level; intimate familiarity with network protocols (TCP/IP, BGP, DNS, HTTP/S, TLS), routing, ASN ecosystems, and complex enterprise network configurations
- Strong Quantitative Acumen: Proven ability to apply mathematical and statistical concepts to real-world problems; ability to design quantitative metrics and build logic that scores and sizes risk exposure accurately
- Analytical Mindset: Exceptional problem-solving skills with track record of conceptualizing complex, abstract security concepts and breaking them down into measurable data points
- Educational Background: Bachelor's or Master's degree in Computer Science, Computer Engineering, Mathematics, Statistics, or related highly quantitative/technical field
- Experience: 3–5+ years of relevant work experience in network engineering, cyber threat research, risk modeling, or related domain
PREFERRED SKILLS:
- Coding for Signal Creation: Proficiency in Python or R to prototype algorithms and turn theoretical risk concepts into programmatic outputs
- Data Querying: Experience with SQL and relational databases to pull, manipulate, and analyze large datasets of asset and configuration data
- Big Data Exposure: Familiarity with big data frameworks (Spark/Hadoop), flat files, complex data types (JSON, XML), and working with APIs
- Industry Context: Experience in or adjacent to cyber insurance, risk modeling, or data science industries
- Certifications: Advanced technical or security certifications (CISSP, CISM, advanced Cisco/networking certifications) are a plus, but hands-on expertise matters most
The company values AI fluency and curiosity about applying AI tools effectively. Candidates from diverse backgrounds are encouraged to apply—you might come from security research, threat research, penetration testing, offensive security, network engineering, detection engineering, attack surface management, bug bounty, red team work, or security data/risk modeling.
