A growing cyber-focused team is hiring multiple Data Modelers to help design and evolve predictive models that support insider risk detection and analysis. This work goes beyond traditional reporting or static statistical modeling and focuses on identifying dynamic patterns, behavioral interactions, and emerging risk signals across large-scale data environments. This role is ideal for someone who combines strong coding ability with advanced quantitative thinking. You will work across raw and metric data, contribute to model development in distributed environments, and help shape how modern behavioral and predictive modeling is applied to complex enterprise risk challenges. What You'll TackleBuild and refine predictive models that support insider risk and cyber-related use cases Work across raw and transformed datasets to develop meaningful model inputs and outputs Analyze dynamic behavioral patterns and interaction-driven signals Contribute to AI/ML workflows in a modern distributed data environment Develop and test model logic using Python, PySpark, and related tools Partner with technical teams to support scalable model development Help shape emerging approaches to behavior-based risk detection Operate within Databricks and distributed processing frameworks to support advanced analytics What You BringStrong computer science or technical engineering foundation Experience developing predictive or behavior-driven models Proficiency in Python Working knowledge of Java is a plus Hands-on experience with Databricks and PySparkExposure to distributed data processing and scalable analytics workflows Background in AI/ML, data science, and applied statistical methods Ability to work with complex datasets and ambiguous problem spaces Strong problem-solving skills and comfort with evolving model design Ability to explain technical concepts and modeling decisions clearly
