Data Scientist Insider Risk Analytics

Zohorecruit; Jersey City (NJ); Hybrid; USD 120,000 - 170,000; Full time

Job summary

Zohorecruit in Jersey City, NJ is seeking a senior Data Scientist to help detect insider risk and drive enterprise risk analytics in a regulated financial services tech environment.

You will design and implement models to identify insider risk across employees, contractors, vendors, and non-human identities, translating signals into actionable insights for leadership.

Qualifications

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

Responsibilities

• Design, build, and refine quantitative models to identify 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 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 findings to both technical and non-technical stakeholders, including senior leadership.

Skills

Data science

Risk analytics

Cybersecurity analytics

Statistical modeling

Communication

Education

Bachelor's or Master's in Data Science/Statistics/Math/Economics/Quant Finance/CS

Tools

Python

R

SQL

Job description

Jersey City, United States | Posted on 10/05/2026

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.