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, Risk Intelligence & AI to define and build machine-learning–driven risk scoring, prioritization, and AI-powered security workflows within our SaaS security platform.

In this role, you will apply statistical modeling, machine learning, and modern AI techniques to help transform security signals into actionable prioritization, and to develop intelligent, agent-driven product capabilities that assist customers with investigation, triage, and response. You will play a key role in shaping how AI is applied responsibly and effectively in customer-facing security workflows.

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 ML and AI systems that are explainable, reliable, and safe to deploy in production.

 

What You’ll Do

• Design and implement data-driven risk scoring and prioritization approaches across SaaS security signals.
• Lead the development of AI-powered product capabilities, including agentic 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 iteration.
• Collaborate with Product to shape AI-driven user experiences, ensuring alignment with customer needs and trust expectations.
• Act as a technical leader and thought partner on applied ML and AI across the product area.

 

What We’re Looking For

• 7–10+ years of experience as a Data Scientist, Applied Scientist, or Machine Learning Engineer, with ownership of production systems.
• Experience in security, identity, fraud, or risk modeling domains. 
• Strong background in statistical modeling, machine learning, and applied decision systems.
• Experience designing and shipping ML- or AI-driven product features used by customers.
• Experience applying ML or AI to decision-making systems that influence user workflows or automated outcomes.
• Comfortability working within the GCP stack, particularly big data services, such as Dataproc (pyspark), Dataflow (Apache Beam), PubSub (Apache Kafka), data lakes (storage, partitioning, searching). Also, experience with SQL, Python, and related Data Science libs (such as scikit-learn, pytorch, GCP integrations, etc).
• Experience designing or contributing to agent-like or automated workflows, including reasoning about task decomposition, tool usage, or control flow.
• Demonstrated ability to design guardrails and human-in-the-loop mechanisms for automated or AI-assisted actions.
• Experience operating ML or AI systems post-launch, including monitoring behavior, iterating based on feedback, and addressing reliability or trust issues.
• Familiarity with LLMs and agent-based approaches, with practical awareness of reliability, safety, and evaluation considerations.
• Ability to balance automation, e