Title:ย Quantitativeย Analyst
Duration:ย 6+ย Months
Location:ย Jersey City, NJ, 07311
Summary:
The Insider Risk team, in partnership with the Information Security Data Operations team, is working on a project to centralize IR data in the Cybersecurity Data Lakehouse (CyberDW). We are looking for a Data Scientist who can work with the developers and Data Analysts to perform analytics, develop risk and quant models around Insider Risk data. Ultimately, we want to create a human risk score for the Insider Risk program. This individual will be adept at ML, AI, and best practices around the new tools in the marketplace.
The Data Scientist / Data Modeler /ย Quantitativeย Analyst will play a critical role in advancing the Insider Risk program's detection, scoring, and decisioning capabilities. This role is responsible for designing, building, and continuously improvingย quantitativeย models, statistical methods, and analytical frameworks used to identify, assess, and prioritize insider risk across employees, contractors, vendors, and nonโhuman identities.
The role partners closely with Cyber, HR, Legal, Compliance, AntiโFraud, and Enterprise Information Protection to transform complex enterprise data into defensible risk signals, transparent scoring models, and executiveโlevel metrics that support investigations, governance, and regulatory scrutiny.
Required Skills:
1) Bachelor's or Master's degree in Data Science, Statistics, Applied Mathematics, Economics,ย Quantitativeย Finance, Computer Science, or a related discipline.
2) 5+ years of experience in data science,ย quantitativeย analysis, or risk modeling, preferably in financial services or regulated industries.
3) Strong experience building statistical or machineโlearning models (regression, classification, anomaly detection, clustering).
4) Proficiency in Python and/or R, with experience in SQL for largeโscale data analysis.
5) Handsโon experience working with complex enterprise datasets and translating analytics into business decisions.
6) Strong communication skills with the ability to explain complex analytical concepts to nonโtechnical stakeholders.
7) Experience supporting Insider Risk, Fraud, AML, Cybersecurity, UEBA, or Threat Analytics programs.
8) Familiarity with identity and access data, endpoint telemetry, DLP, email, or collaboration monitoring.
9) Experience with model explainability, governance, and validation in regulated environments.
10) Knowledge of employee lifecycle risk, behavioral analytics, or humanโcentric risk modeling.