Data Modeler
Role details
Job location
Tech stack
Job description
Genesis10 is currently seeking a Data Modeler for a hybrid position with a Global Financial Institution located in Charlotte, NC. This is a 6 month contract opportunity.
This role will support the Insider Risk program's project to centralize data in the Cybersecurity Data Lakehouse. The Data Scientist will be responsible for designing and building quantitative models using statistical methods and analytical frameworks to identify and assess insider risk. This position will partner with Cyber, HR, Legal, and other teams to transform complex enterprise data into defensible risk signals and scoring models.
Responsibilities:
- Perform analytics and develop risk and quantitative models around Insider Risk data
- Design, build, and continuously improve quantitative models, statistical methods, and analytical frameworks to identify, assess, and prioritize insider risk
- Partner closely with Cyber, HR, Legal, Compliance, Anti-Fraud, and Enterprise Information Protection teams
- Transform complex enterprise data into defensible risk signals, transparent scoring models, and executive-level metrics
- Create a human risk score for the Insider Risk program using ML and AI
Requirements
- Bachelor's or Master's degree in Data Science, Statistics, Applied Mathematics, Economics, Quantitative Finance, Computer Science, or a related discipline
- 5 years of experience in data science, quantitative analysis, or risk modeling, preferably in financial services or regulated industries
- Strong experience building statistical or machine learning models (regression, classification, anomaly detection, clustering)
- Proficiency in Python and/or R, with experience in SQL for large-scale data analysis
- Hands-on experience working with complex enterprise datasets and translating analytics into business decisions
- Strong communication skills with the ability to explain complex analytical concepts to non-technical stakeholders
- Experience supporting Insider Risk, Fraud, AML, Cybersecurity, UEBA, or Threat Analytics programs
- Familiarity with identity and access data, endpoint telemetry, DLP, email, or collaboration monitoring
- Experience with model explainability, governance, and validation in regulated environments
- Knowledge of employee lifecycle risk, behavioral analytics, or human-centric risk modeling