Lead Data Scientist

<h3>About AppOmni</h3> <p>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.</p> <p>Recognized as a <a href="https://drive.google.com/drive/folders/1sYC94rvhwD9ARzvgwwaxcs4upkp6acAZ"><em>Frost Radar™ 2025 Leader</em></a><em> and</em><a href="https://www.greatplacetowork.com/certified-company/7078597"><em> Great Place To Work</em></a><em>®</em>, 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.</p> <p> </p> <h3><strong>About the Role</strong></h3> <p>AppOmni is looking for a Lead Data Scientist, Risk Intelligence & AI to engineer, develop, and operationalize data-driven risk scoring, signal prioritization, and agent-driven security workflows within our SaaS security platform.</p> <p>In this role, you will apply statistical modeling, probabilistic reasoning, machine learning, and modern AI techniques to transform complex SaaS security signals into actionable prioritization. You will help develop intelligent product capabilities that assist customers with investigation, triage, and response, while ensuring that risk calculations and AI-assisted recommendations remain explainable, reliable, and trustworthy.</p> <p>This is a hands-on individual contributor role with technical leadership responsibilities. You will work closely with Product and Engineering to design and ship production systems that combine data science, explainable decision logic, and AI where appropriate. You should be comfortable operating in ambiguity, testing ideas quickly, and translating uncertain or incomplete evidence into practical customer-facing workflows.</p> <p> </p> <h3><strong>What You’ll Do</strong></h3> <ul> <li>Design and implement data-driven risk scoring and prioritization approaches across diverse SaaS security signals.</li> <li>Develop explainable decision logic that helps customers understand why issues are prioritized or actions are recommended.</li> <li>Apply statistical modeling, probability, heuristics, machine learning, and AI techniques where appropriate to improve risk assessment and prioritization.</li> <li>Contribute to approaches that assess the potential scope, likelihood, confidence, and impact of security issues.