Data Scientist Insider Risk Analytics

Axiom Path • Jersey City, NJ

11h • Hybrid

About The Position

The Data Scientist, Insider Risk Analytics will work in a sophisticated financial services technology environment supporting cybersecurity, data operations, and enterprise risk management. The position contributes to a high-visibility program focused on centralizing insider risk data and turning complex behavioral and enterprise signals into actionable insights.

Based in Jersey City, New Jersey, this hybrid role works across Cybersecurity, HR, Legal, Compliance, Anti-Fraud, and enterprise protection teams. The Data Scientist helps shape risk scoring, model governance, and executive-level reporting in a highly regulated environment. The engagement is W-2 only; C2C and 1099 arrangements are not accepted.

Requirements

• At least 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.
• Strong communication skills and ability to simplify complex analytical concepts for non-technical stakeholders.

Nice To Haves

• Knowledge of employee lifecycle risk, behavioral analytics, or human-centric risk modeling.

Responsibilities

• Design, build, and refine quantitative models to 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior