Senior Data Scientist – Insider Risk & Cyber Analytics #3588476

Join a globally recognized financial institution operating at the forefront of banking, risk management, and technology innovation. The organization is known for its strong governance, data-driven decision-making, and investment in advanced cybersecurity initiatives.

This team sits within a specialized Insider Risk and Cybersecurity Data Operations function, working on cutting-edge initiatives to centralize and transform enterprise data into actionable intelligence. The environment is highly collaborative, partnering across Cybersecurity, HR, Legal, Compliance, and Fraud teams to build sophisticated risk models and analytics frameworks. The team is focused on developing next-generation capabilities such as human risk scoring and behavioral analytics to strengthen enterprise security posture.

• Engagement: W2 only (no C2C/1099)
• Opportunity to work on advanced AI/ML-driven cybersecurity initiatives
• Exposure to enterprise-wide data strategy and executive-level decisioning
• Hybrid work environment with collaboration across multiple business units
• High-impact role contributing to regulatory, risk, and security outcomes

• Design and develop quantitative models to detect, assess, and prioritize insider risk across enterprise environments
• Build and enhance machine learning models (classification, anomaly detection, clustering) for cybersecurity and risk analytics
• Partner with cross-functional teams to translate complex data into actionable insights and risk signals
• Contribute to the development of a centralized Cybersecurity Data Lakehouse (CyberDW)
• Create transparent and explainable risk scoring models for governance and regulatory alignment
• Support decision-making through advanced analytics, reporting, and executive-level metrics

• 5+ years of experience in data science, quantitative analysis, or risk modeling (10+ years preferred)
• Strong background in machine learning techniques such as regression, classification, clustering, and anomaly detection
• Proficiency in Python and/or R, along with strong SQL skills for large-scale data analysis
• Experience working with complex enterprise datasets and translating insights into business decisions
• Background in cybersecurity, insider risk, fraud, AML, UEBA, or threat analytics programs
• Familiarity with identity/access data, endpoint telemetry, DLP, or communication monitoring systems
• Experience with model governance, validation, and explainability in regulated environments
• Strong communication skills with the ability to explain technical concepts to non-technical stakeholders
• Bachelor’s or Master’s degree in a quantitative field such as Data Science, Statistics, Computer Science, or related discipline

#dice