Principal Data Scientist

Role Overview

Join AppOmni as a Principal Data Scientist (hands-on, individual contributor with technical leadership). You will define and build machine-learning-driven risk scoring, prioritization, and AI-powered security workflows for a SaaS security platform, working with Product and Engineering to operationalize production ML/AI systems.

Responsibilities

• Design and build ML-driven risk scoring, prioritization, and AI security workflows within the SaaS security platform.
• Lead development of AI-powered product capabilities, including agentic and LLM-based features for investigation, triage, and security operations workflows.
• Operationalize ML/AI systems with a focus on explainability, reliability, and safety in production.
• Partner with Product and Engineering to deliver customer-facing ML/AI capabilities end-to-end.
• After launch, monitor ML/AI behavior, iterate based on feedback, and address reliability/trust issues.
• Design guardrails and human-in-the-loop (HITL) mechanisms for automated or AI-assisted actions.

Requirements

• 7–10+ years experience as a Data Scientist, Applied Scientist, or Machine Learning Engineer, with ownership of production systems.
• Experience designing or contributing to agent-like or automated workflows, including task decomposition, tool usage, and control flow reasoning.
• Strong background in statistical modeling, machine learning, and applied decision systems.
• Ability to balance automation, explainability, and user trust in customer-facing systems.
• Experience applying ML/AI to decision-making systems that influence user workflows or automated outcomes.
• Practical familiarity with LLMs and agent-based approaches, including reliability, safety, and evaluation considerations.
• Demonstrated ability to ship ML/AI-driven product features used by customers.
• Strong written and verbal communication skills.

Nice-to-Haves / Domain

• Experience applying ML/AI in security, identity, fraud, or risk modeling domains.
• Familiarity with security-appropriate evaluation and safety practices for automated actions.

Technical Requirements (GCP stack)

• Comfort working within GCP, especially big data services such as:
  • Dataproc (PySpark)
  • Dataflow (Apache Beam)
  • Pub/Sub (Apache Kafka)
  • Data lakes (storage, partitioning, searching)
• Experience with SQL and Python and common ML libraries such as scikit-learn and PyTorch, plus GCP integrations.