Description

What's the Job?

• Collaborate with cross-functional teams including Cybersecurity, HR, Legal, and Compliance to develop risk models and analytical frameworks.
• Design, build, and refine quantitative models, statistical methods, and machine learning algorithms to identify insider risks.
• Translate complex enterprise data into clear risk signals and executive-level metrics to support investigations and governance.
• Support the development of human risk scores and other predictive analytics tools to enhance insider threat detection capabilities.
• Ensure model explainability, governance, and validation in accordance with regulatory standards and best practices.

What's Needed?

• Bachelor’s or Master’s degree in Data Science, Statistics, Applied Mathematics, Economics, Quantitative Finance, Computer Science, or related field.
• 5+ years of experience in data science, risk modeling, or quantitative analysis, preferably within regulated industries.
• Proficiency in building statistical or machine learning models such as regression, classification, anomaly detection, and clustering.
• Hands-on experience with Python and/or R, along with SQL for large-scale data analysis.
• Strong communication skills to effectively explain complex analytical concepts to non-technical stakeholders

Key Skills

Education

Any Graduate