Cyber Risk Quantification Product Manager

Job description

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Cyber Risk Quantification Product Manager The Cyber Risk Quantification Product Manager role is designed for a candidate with strong experience in quantitative cyber risk analysis, FAIR-based methodologies, and product-oriented problem-solving. The role offers a unique opportunity to collaborate with leading cybersecurity, risk, product, analytics, and engineering experts within our cybersecurity products organization. The ideal candidate will combine cyber risk expertise, analytical rigor, and cross-functional collaboration skills to help develop clear, scalable, and defensible cyber risk quantification capabilities. This role requires someone who can work across cybersecurity, risk, product, analytics, and engineering teams to translate cyber risk concepts into product requirements, analytical approaches, and clear user-facing explanations. The position is well suited for a hands-on product professional who understands quantitative risk modeling and can make complex methodologies usable for business and technical audiences. The successful candidate will bring practical experience in cyber risk quantification, model development, model validation, financial impact analysis, and methodology documentation, with the ability to support both strategic planning and day-to-day execution. Role Overview The ideal candidate is a strategic and analytical thinker who combines hands-on problem-solving with strong product judgment and serves as a subject-matter expert in cyber risk quantification and cyber risk analytics, with experience applying FAIR-based models, including FAIR, FAIR-CAM, and FAIR-MAM. The candidate will help translate cyber risk quantification methodologies, model assumptions, assessment insights, control effectiveness concepts, financial impact analysis, and loss-data considerations into clear product requirements, analytical logic, and documentation. The right candidate understands how quantitative cyber risk assessments work in real-world environments and can use that insight to improve methodology clarity, model usability, and product applicability. The candidate is a fast learner who takes ownership, independently drives initiatives, collaborates effectively across teams, and applies a creative, solution-oriented mindset to complex cyber risk challenges. Key Responsibilities: • Support the development, calibration, validation, and explanation of FAIR-based cyber risk quantification and ROI models, including FAIR, FAIR-CAM, and FAIR-MAM. • Translate cyber risk quantification methodologies into clear product requirements, analytical logic, model assumptions, and user-facing explanations. • Apply cybersecurity, threat, exposure, control, and financial-impact concepts to support practical cyber risk quantification use cases and model inputs. • Work with product, engineering, analytics, cybersecurity, and risk stakeholders to refine requirements, validate assumptions, and support practical application of quantitative risk capabilities. • Maintain clear documentation that explains how models work, what assumptions are used, and how results, recommendations, and analytical outputs should be interpreted. Qualifications Required Qualifications: • 5+ years of hands-on experience in cyber risk quantification, quantitative cyber risk assessment, or related cyber risk analytics roles. • Practical experience applying FAIR-based methodologies, including FAIR, FAIR-CAM, or FAIR-MAM, to cyber risk scenarios, assessments, or product use cases. • Experience building, calibrating, validating, or explaining quantitative risk models, including model assumptions, inputs, outputs, and limitations. • Strong understanding of model governance, validation, documentation, assumptions management, explainability, and methodology defensibility for quantitative cyber risk models. • Strong foundation in statistics, probability, financial impact estimation, and ROI analysis, with the ability to apply these skills to cyber risk quantification. • Working knowledge of cybersecurity and risk management frameworks such as NIST, ISO 27000-series standards, MITRE ATT&CK, exposure management concepts, and related standards. • Experience working with product, engineering, analytics, cybersecurity, or risk teams to translate analytical concepts into practical product requirements and supporting documentation. • Strong analytical, problem-solving, communication, collaboration, and project management skills, with the ability to balance structured execution and creative thinking. • Fluent in English, with the ability to explain complex cyber risk concepts clearly to both technical and non-technical audiences. Preferred Qualifications / Advantages: • Experience applying FAIR-based methodologies in enterprise, consulting, or product environments is an advantage. • Experience with cyber threat intelligence, exposure management, vulnerability management, control effectiveness analysis, or cyber loss data is an advantage. Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.