Data Modeler

Description:
The Insider Risk team in partnership with the Information Security Data Operations team are 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 / Quantitative 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 improving quantitative models, statistical methods, and analytical frameworks used to identify, assess, and prioritize insider risk across employees, contractors, vendors, and non-human identities.
The role partners closely with Cyber, HR, Legal, Compliance, Anti-Fraud, and Enterprise Information Protection to transform complex enterprise data into defensible risk signals, transparent scoring models, and executive-level metrics that support investigations, governance, and regulatory scrutiny.

Required Skills:
1) Bachelor's or Master's degree in Data Science, Statistics, Applied Mathematics, Economics, Quantitative Finance, Computer Science, or a related discipline.
2) 5+ years of experience in data science, quantitative analysis, or risk modeling, preferably in financial services or regulated industries.
3) Strong experience building statistical or machine-learning models (regression, classification, anomaly detection, clustering).
4) Proficiency in Python and/or R, with experience in SQL for large-scale data analysis.
5) Hands-on 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 non-technical 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 human-centric risk modeling.