CyberCube provides software-as-a-service cyber risk modeling and analytics tools for insurers, reinsurers, and brokers.
About CyberCube: https://www.cybcube.com/careers CyberCube delivers the world's leading analytics to quantify cyber risk, translating one of the most critical risks of today and the future into financial impact for businesses, markets, and society. - AI at our foundation, cyber risk at our core. We don't just use AI, we shape it. - Built on AI from day one. Artificial intelligence has been part of our strategy since the beginning, blended with deep cybersecurity and insurance expertise and backed by rigorous testing. - Trusted by more than 100 clients, including 75% of the top 40 European and US cyber insurance carriers and 70% of the top ten reinsurance brokers. - Backed for global growth. Led by investors including Forgepoint Capital, with a $180+ million investment in 2025 from new cornerstone investor Spectrum Equity. - A truly global team across San Francisco, New York, London, and Tallinn. - A culture of collaboration, openness, intellectual rigor, and ownership for excellence. - People at the forefront. We encourage CyberCubers to challenge themselves, push boundaries, and do the best work of their careers. As a key member of the Cyber Risk Modeling (CRM) team, you will research and analyze large, complex cybersecurity datasets to engineer analytical models for the insurance industry. The CRM team builds cyber risk models that leading insurers and reinsurers rely on for single-risk and portfolio decisions. You will work closely with our Actuarial, Data Science, Data Engineering, and Application Engineering teams to take those models from research into production. This is a quantitative modeling role with a cyber lens, not a hands-on security job. The cyber side informs the work; the core of the role is translating cyber principles into rigorous statistical models. We want someone quantitative and adaptable who is excited to work at the intersection of modeling, cyber, and insurance. RESPONSIBILITIES - Build, validate, and refine defensible analytical cyber risk models for single-risk and aggregate risk products in the insurance industry. - Refine and build technographic models that integrate cybersecurity, insurance, and risk modeling through research and large datasets, bringing in new technologies, data sources, and techniques to make the models more valuable and representative. Areas of work include cybersecurity posture, cloud security, malware defense, cyber risk exposure, technology stack dependencies, security practices, and threat actor characteristics. - Translate cyber principles and large, complex datasets (including threat intelligence) into model inputs and financial measures: frequencies, severities, probabilities, and the trends that drive loss over time. - Work closely with the product, analytics, engineering, and client success teams in day-to-day tasks and projects. - Look for new and creative ways to bring AI into your work, and share what works with the team. - Present models and findings to internal teams, and on occasion to clients, explaining outputs and loss drivers in plain terms. - Contribute robust internal and external documentation in the form of model documents, industry studies, informational videos, and code comments. - Support cyber catastrophe model clients through change management, and channel their questions and feedback to the Product & Analytics team to shape future model direction. SKILLS & QUALIFICATIONS - Self-starter able to work well in independent and various team settings, including with teammates in other time zones. - Intellectual curiosity with willingness to learn new skills and contribute ideas. - Demonstrated quantitative modeling experience. You have built or worked on predictive or statistical models, whether in econometrics, statistics, or internal business modeling, and worked with large datasets. - Eager to work in an agile environment, with the ability to pick up and drop tasks as priorities shift and questions arise. - Strong written and verbal communication, including summarizing technical analysis for decision makers who are not technical, using dashboards, charts, or tools like Tableau. - A genuine interest in cybersecurity. Early-stage knowledge is fine; curiosity and aptitude matter more than years of practice. - Programming literacy. You have read and written Python and a query language such as SQL, enough to follow and interpret code in a live setting. You do not need to be an expert developer. - Sound judgment about working with AI. You know when it genuinely helps and when it does not, you can get useful results from it, you check its output against the source, and you stand behind whatever you produce with it. - Degree in a quantitative or technical field such as statistics, economics or econometrics, mathematics, data science, or computer science. EXTRA CREDIT - Experience with catastrophe or risk quantification models. - Awareness of commercial insurance concepts, including cyber insurance, loss ratios, or calculating losses with probabilities and frequencies. - Graduate degree in a related quantitative or engineering discipline such as mathematics, actuarial science, statistics, data engineering or computer science. - Familiarity with database schemas and queries in SQL or NoSQL. - Experience with data visualization in Tableau, Python, R, or Excel. - Experience working in an agile team. - Experience at a startup or scaleup. OUR INTERVIEW PROCESS We aim to be transparent and respectful of your time. The process is typically: - Recruiter screen (30 min): your background, motivation, and the role, plus logistics and compensation. - Hiring manager conversation (30 min): the role in depth, the team, and what success looks like. - A series of 30-60 minute conversations with team members covering the core areas of the role, including technical depth, communication, and cross-functional collaboration. WHY YOU'LL LOVE IT HERE (US) -