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
6+ 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