Tech Stack

Description

You will define and build machine-learning-driven risk scoring, prioritization, and AI-powered security workflows within a SaaS security platform. You'll apply statistical modeling and modern AI techniques to transform security signals into actionable prioritization and develop intelligent, agent-driven product capabilities for investigation, triage, and response.

Requirements

• 7-10+ years as a Data Scientist, Applied Scientist, or ML Engineer with production system ownership
• 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
• Comfortable with GCP stack (Dataproc, Dataflow, PubSub), SQL, Python, and data science libraries

Responsibilities

• Design and implement data-driven risk scoring and prioritization across SaaS security signals
• Lead development of AI-powered product capabilities including agentic and LLM-based features for security workflows
• Define explainable decision logic for issue prioritization and recommended actions
• Establish evaluation methods to measure model quality and effectiveness over time
• Partner with Engineering to operationalize models and AI workflows, supporting safe deployment and iteration