Data Scientist – Insider Risk Analytics

Be Part Of A High-Performing Team

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.