THE OPPORTUNITY:

Are you constantly exploring more data-driven and rigorous approaches for analyzing the range of cyber-attacks, internet disruptions and manipulations, digital supply chain vulnerabilities, and shifting security and privacy regulatory and threat environments? Do you seek opportunities to transform complex, quantitative information security models into useful insights for a range of public and private sector customers? Are you looking to apply all these skills at a high-growth, start-up focused on supply chain risk?
At Interos, we are seeking a Senior Data Scientist, Cyber Risk who has a deep understanding of the hard work, patience, and diligence required to build, implement, and productize quantitative models of complex and large systems. With a focus on risk modeling across global supply chains, the Senior Data Scientist, Cyber Risk will operate, assess, and innovate on theory- and data-driven computational cyber risk models that expands beyond the logs to apply novel information security data and statistical models that are relevant across the range of digital supply chain risks. An avid researcher and writer, the Senior Data Scientist, Cyber Risk is also a data storyteller, with strong communication skills to both technical and non-technical audiences.
Join the growing Applied AI team at Interos and help implement cutting-edge risk models while working in a fast paced, multi-disciplinary environment. Introduce and operationalize complex models at scale for our private and public sector customers, while collaborating with a growing team focused on various cyber components of supply chain risk. Contribute to model-focused projects that help augment product features within the Interos platform. This role collaborates within the Applied AI team and across engineering and product and is responsible for navigating cyber risk model development from inception through to productization.

MINIMUM QUALIFICATIONS:

• A PhD in social, computer, data, or behavioral science or related discipline, or at least three years of work experience modeling and operationalizing quantitative cyber models, research, and analyses.
• A curious researcher and analyst who never stops seeking better data and new solutions to complex socio-technical and physical challenges.
• A passion for technology and the scientific application of quantitative models to social and physical systems, and the impact of emerging technologies.
• Strong communication skills, verbal and written.
• Proven open-source research skills and supporting product development.
• A team player who enjoys multi-disciplinary collaboration in an extremely fast-paced and fluid environment.
• Familiarity with Python, Jira, Git or other data analysis and engineering collaboration tools.

PREFERRED QUALIFICATIONS:

• Ability to productize rigorous technical and/or academic models within a private sector or operational work environment.
• Background in product- and customer-oriented research and feature development.
• Demonstrable background interacting and communicating with a range of audiences (internal and external; technical and business stakeholders).
• Experience applying quantitative cyber risk models to supply chains at both a localized and global level of analysis.
• The ability to adapt quickly to shifting priorities and multi-task under tight deadlines.

• Research and implement computational cyber risk models for integration into the Interos platform as part of the Applied AI team.
• Collaborate with product and engineering team members and effectively communicate the models and underlying components.
• Conduct independent research and validation of diverse models from across multiple disciplines and assess their applicability to current product requirements.
• Translate academic and industry research into applied and scalable supply chain risk models with a focus on digital supply chain risks.
• Stay abreast of the latest current events, data sources, and research methods as they relate to global supply chains and the full range of internet and data trends and disruptions.