Data Scientist – Insider Risk Analytics #3594769

Join a sophisticated financial services technology environment supporting cybersecurity, data operations, and enterprise risk management initiatives. This team is focused on strengthening how insider risk is detected, measured, and governed across a large, regulated organization. The role sits at the intersection of cybersecurity, data science, analytics, and risk decisioning, contributing to a high-visibility program designed to centralize insider risk data and transform complex behavioral and enterprise signals into actionable insights.

This is a hybrid opportunity based in Jersey City, NJ, supporting a cybersecurity data lakehouse initiative tied to insider risk and advanced analytics. The role offers the opportunity to work across Cybersecurity, HR, Legal, Compliance, Anti-Fraud, and enterprise protection teams while helping shape risk scoring, model governance, and executive-level reporting for a highly regulated environment.

• Design, build, and refine quantitative models that help identify, assess, and prioritize insider risk across employees, contractors, vendors, and non-human identities.
• Partner with data engineers, analysts, cybersecurity stakeholders, and business teams to centralize insider risk data within a cybersecurity data lakehouse.
• Develop statistical, machine learning, and analytical frameworks for anomaly detection, classification, clustering, scoring, and behavioral risk modeling.
• Translate large, complex enterprise datasets into clear risk signals, defensible models, and actionable business recommendations.
• Support the creation of human-centric risk scoring methodologies that improve detection, investigations, governance, and regulatory readiness.
• Communicate model outputs, assumptions, and analytical findings to technical and non-technical stakeholders, including senior leadership.

• 5+ years of experience in data science, quantitative analysis, statistical modeling, or risk analytics.
• Bachelor’s or Master’s degree in Data Science, Statistics, Applied Mathematics, Economics, Quantitative Finance, Computer Science, or a related discipline.
• Strong experience developing statistical or machine learning models, including regression, classification, anomaly detection, and clustering.
• Proficiency with Python and/or R, plus strong SQL skills for large-scale data analysis.
• Experience working with complex enterprise datasets and translating analytics into operational or business decisions.
• Background supporting Insider Risk, Fraud, AML, Cybersecurity, UEBA, Threat Analytics, or related risk programs.
• Familiarity with identity/access data, endpoint telemetry, DLP, email, collaboration monitoring, or similar enterprise security datasets.
• Understanding of model explainability, governance, validation, and documentation expectations in regulated environments.
• Knowledge of employee lifecycle risk, behavioral analytics, or human-centric risk modeling is strongly preferred.
• Strong communication skills with the ability to simplify complex analytical concepts for non-technical stakeholders.