Data Scientist Insider Risk Analytics

Company: Axiom Path

Role summary

Data Scientist Insider Risk Analytics is a contract role at Axiom Path in Jersey City, New Jersey, United States (hybrid). Posted 2026-10-05. Experience level: 5-10.

Job title
Data Scientist Insider Risk Analytics
Company
Axiom Path
Location
Jersey City, New Jersey, United States
Work arrangement
hybrid
Employment type
contract
Category
Data Science
Experience level
5-10
Date posted
2026-10-05

This is a fantastic contract opportunity to join a top-tier financial tech team working on critical cybersecurity and risk management initiatives. You'll be instrumental in developing advanced ways to detect and manage insider risks within a large, regulated organization. If you're passionate about data science and making a real impact in enterprise security, this role offers high visibility and significant professional growth.

Job description

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.

What's In Store For You:

Engagement: W2 only (no C2C/1099)

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.

How You Will Make An Impact

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.

Requirements

Do you bring proven success in data science, risk modeling, and cybersecurity analytics?

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.

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