Data Science Lead, RQ (Remote - Virginia)

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.

• Competitive salary and performance-based incentives.
• Comprehensive health, dental, and vision insurance coverage.
• Generous paid time off and flexible vacation policy.
• Opportunities for professional growth, training, and mentorship.
• Remote work flexibility with occasional on-site collaboration.
• Collaborative, innovation-driven environment with exposure to cutting-edge AI and cybersecurity technologies.

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly:
🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.
🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.

The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.

Thank you for your interest!

#LI-CL1