Lead Data Scientist

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 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. 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. 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. What You’ll Do Design and implement data-driven risk scoring and prioritization approaches across diverse SaaS security signals. Develop explainable decision logic that helps customers understand why issues are prioritized or actions are recommended. Apply statistical modeling, probability, heuristics, machine learning, and AI techniques where appropriate to improve risk assessment and prioritization. Contribute to approaches that assess the potential scope, likelihood, confidence, and impact of security issues. Lead development of AI-assisted product capabilities, including LLM-based and agent-like workflows that support investigation, triage, and security operations. Establish evaluation methods to measure model quality, decision quality, reliability, explainability, and customer trust over time. Incorporate product usage signals, customer feedback, and expert input to guide continuous improvement of risk, ML, and AI systems. Monitor production systems to ensure stability, safety, consistency, and appropriate behavior across customers and use cases. Partner with Engineering to operationalize scoring logic, models, data pipelines, and AI workflows, supporting safe deployment and iteration. Collaborate with Product to shape customer-facing risk and AI experiences that balance automation, explainability, and user trust. Act as a technical leader and thought partner on applied data science, risk intelligence, and AI across the product area. What We’re Looking For 7+ years of experience as a Data Scientist, Applied Scientist, Machine Learning Engineer, or similar role, with ownership of production data or decision systems. Strong foundation in probability, statistics, applied mathematics, physics, operations research, or a related quantitative field. Experience designing practical decision systems that operate under uncertainty, incomplete labels, noisy signals, or subjective expert judgment. Strong background in statistical modeling, applied decision systems, and machine learning, with good judgment about when ML is and is not the right tool. Experience designing and shipping data-driven, ML-driven, or AI-assisted product features used by customers. Experience with decision-making systems that influence prioritization, triage, investigation, recommendations, user workflows, or automated outcomes. Experience in security, identity, fraud, abuse, trust & safety, risk modeling, or related domains is strongly preferred. Comfort working within the GCP stack, particularly big data services such as Dataproc/PySpark, Dataflow/Apache Beam, Pub/Sub or Kafka-like streaming systems, and data lakes. Experience with SQL, Python, and data science libraries such as scikit-learn, PyTorch, or related tools is expected. Experience designing or contributing to LLM-based, agent-like, or automated workflows, including reasoning about task decomposition, tool usage, control flow, and failure modes. Demonstrated ability to design guardrails, evaluation methods, and human-in-the-loop mechanisms for automated or AI-assisted actions. Experience operating data, ML, or AI systems post-launch, including monitoring behavior, iterating based on feedback, and addressing reliability, safety, or trust issues. Ability to balance automation, explainability, statistical rigor, and customer trust in production systems. Strong partnership skills with Product and Engineering, including the ability to translate ambiguous product goals into testable data science approaches. High ownership, curiosity, bias toward action, and willingness to dig into unfamiliar problems, test ideas, and learn the domain deeply. Strong written and verbal communication skills. Culture Our talented team is collaborative and supportive as we move quickly to research and develop new ideas, deliver new features to our customers, and iterate on ideas and innovations. We accomplish this by focusing on our five core values: Trust, Transparency, Quality, Customer Focus, and Delivery. Our team is determined to make a difference to positively impact our way of life by securing the technology that i