Principal Data Scientist - Risk Intelligence & AI

About AppOmni

AppOmni prevents SaaS data breaches by delivering end-to-end SaaS security. Our platform gives security teams clear visibility into posture, access, third-party connections, AI-related activity, and with built-in discovery to identify unsanctioned SaaS and Shadow AI tools. Backed by continuous monitoring and real-time threat detection, AppOmni helps enterprises identify and resolve risks early, keeping their SaaS applications secure.

Recognized as a Frost Radar™ 2025 Leader and Great Place To Work ® , AppOmni continues to set the standard for innovation and customer value in SaaS security. The largest and fastest-growing global enterprises across industries trust AppOmni to secure their SaaS applications.

About the Role

AppOmni is looking for a Principal Data Scientist to define and build a production data–driven risk scoring, prioritization, and cybersecurity risk system within our SaaS security platform.

In this role, you will apply data science, probability, and statistical modeling, in both batch as well as real-time analytics, to help transform security signals into actionable prioritization, and to develop intelligent, data driven product capabilities that assist customers with investigation, triage, and response. You will play a key role in shaping how advanced modeling is applied responsibly and effectively in customer-facing security workflows. You will be responsible for the end to end production pipeline from data mining, analytics implementation, to production monitoring.

This is a hands-on individual contributor role with technical leadership responsibilities. You will work closely with Product and Engineering to design, build, and operationalize data-driven risk modeling systems that are explainable, reliable, and safe to deploy in production.

What You’ll Do

• Design and implement in production data-driven risk scoring and prioritization approaches across diverse large volumes of SaaS security signals.
• Lead the development and decisions on data-driven product capabilities, including data requirements, data mining, data pre-processing, data analytics, production architecture and LLM-based features that support investigation, triage, and security operations workflows.
• Define and evolve explainable decision logic so customers understand why issues are prioritized or actions are recommended.
• Contribute to approaches that assess the potential scope and impact of security issues.
• Establish evaluation methods to measure model quality, effectiveness, and reliability over time.
• Incorporate product usage signals and feedback to guide continuous improvement of ML and AI systems.
• Monitor ML and AI systems in production to ensure stability, safety, and consistent behavior.
• Partner with Engineering to operationalize models and AI workflows, supporting safe deployment and ite