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Data Science Lead, RQ (Remote - Virginia) at Jobgether. This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Data Science Lead, RQ in Arlington, VA.. This role offers a unique opportunity to lead advanced data science initiatives in the field of cyber risk and threat intelligence. You will design, implement, and deploy statistical and probabilistic models that quantify risk and predict threats across enterprise environments. The position blends hands-on modeling with team leadership, guiding a growing group of data scientists while collaborating closely with product, engineering, and threat intelligence teams. You will influence the evolution of risk quantification platforms, operationalize AI-enhanced analytics, and ensure models are accurate, explainable, and actionable. This environment values innovation, technical excellence, and strategic thinking, providing a platform to make measurable impact on risk management solutions.. . Accountabilities. . Lead the design, implementation, and deployment of statistical and probabilistic models for threat likelihood, loss magnitude, and control effectiveness.. . Curate, expand, and maintain cyber risk datasets, including loss events, CVE/KEV data, MITRE ATT&CK mappings, control posture, and third-party risk information.. . Mentor and guide a team of data scientists, balancing hands-on modeling with leadership responsibilities.. . Collaborate with Product, Engineering, and Threat Intelligence teams to operationalize models and ensure integration into enterprise platforms.. . Research and apply advanced AI/ML techniques (e.g., Bayesian modeling, pattern mining, LLMs) to enhance model accuracy and automation.. . Ensure model transparency, governance, and explainability to support customer and regulatory review.. . Document model assumptions, methodologies, and best practices to build internal knowledge resources.. . . 7+ years of experience in applied data science, quantitative modeling, or algorithm development.. . Strong understanding of cybersecurity principles, threat actor behavior, and risk frameworks (e.g., NIST CSF, MITRE ATT&CK, FAIR).. . Proven ability to build, validate, and deploy predictive or risk models in enterprise environments.. . Proficiency in Python and familiarity with data science and modeling libraries (NumPy, PyMC3, scikit-learn, etc.).. . Experience with Git, Jira, and modern MLOps pipelines.. . Strong communication and storytelling skills for both technical and non-technical audiences.. . Advanced degree (Master’s or PhD) in Data Science, Computer Science, Engineering, or related field is preferred.. . Familiarity with SaaS platforms, cloud-native environments, and integrating models into production systems is a plus.. . Background in cybersecurity operations, red/blue teaming, or adversary emulation is advantageous.. . . Company Location: United States.