Responsibilities

• Understand complex cybersecurity and business problems, translate them into well-defined data science problems, and build scalable solutions.
• Design and build robust, large-scale graph structures to model security entities, behaviors, and relationships.
• Develop and deploy scalable, production-grade AI/ML systems and intelligent agents for real-time threat detection, classification, and response.
• Collaborate closely with Security Research teams to integrate domain knowledge into data science workflows and enrich model development.
• Drive end-to-end ML lifecycle: from data ingestion and feature engineering to model development, evaluation, and deployment.
• Work with large-scale graph data: create, query, and process it efficiently to extract insights and power models.
• Lead initiatives involving Graph ML, Generative AI, and agent-based systems, driving innovation across threat detection, risk propagation, and incident response.
• Collaborate closely with engineering and product teams to integrate solutions into production platforms.
• Mentor junior team members and contribute to strategic decisions around model architecture, evaluation, and deployment.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Statistics, Applied Mathematics, Data Science, or a related quantitative field
• 7+ years of experience applying data science or machine learning in a real-world setting, preferably in security, fraud, risk, or anomaly detection
• Proficiency in Python and/or R, with hands-on experience in data manipulation (e.g., Pandas, NumPy), modeling (e.g., scikit-learn, XGBoost), and visualization (e.g., matplotlib, seaborn)
• Strong foundation in statistics, probability, and applied machine learning techniques
• Experience working with large-scale datasets, telemetry, or graph-structured data Ability to clearly communicate technical insights and influence cross-disciplinary teams
• Demonstrated ability to work independently, take ownership of problems, and drive solutions end-to-end.