Principal Data Scientist - Risk Intelligence & Ai

AppOmni

Remote
Base: $250,000 - $300,000 usd; bonus/equity: stock...
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Machine learning-driven risk scoring
Ai-powered security workflows
Statistical modeling and modern ai
** AppOmni is seeking a Principal Data Scientist specializing in Risk Intelligence and AI to enhance its SaaS security platform through machine learning and AI innovations. The role is focused on developing risk scoring systems and AI-powered workflows, and it requires extensive experience in data science, security, and collaborative product development. **

Job Summary

  • AppOmni prevents SaaS data breaches by delivering end-to-end SaaS security.
  • Design and implement data-driven risk scoring and prioritization approaches across SaaS security signals.
  • The annual base salary compensation range in the U.S. for this role is: $250,000 - $300,000 USD.

Matching Summary

Match Score: 75

** AppOmni is seeking a Principal Data Scientist specializing in Risk Intelligence and AI to enhance its SaaS security platform through machine learning and AI innovations. The role is focused on developing risk scoring systems and AI-powered workflows, and it requires extensive experience in data science, security, and collaborative product development. **

Salary

Base: $250,000 - $300,000 USD; Bonus/Equity: Stock Options; Benefits: Generous PTO, health insurance, 401(k), wellness reimbursement

Skills & Requirements

Must-have

  • machine learning-driven risk scoring
  • AI-powered security workflows
  • statistical modeling and modern AI
  • explainable, reliable, and safe ML/AI systems
  • GCP stack and big data services
  • Python and data science libraries
  • agent-like or automated workflows
  • monitoring ML/AI systems in production

Nice-to-have

  • customer-facing security workflows
  • collaboration with Product and Engineering
  • balancing automation, explainability, and trust

Key Requirements

  • 7–10+ years of experience as a Data Scientist, Applied Scientist, or Machine Learning Engineer
  • Experience in security, identity, fraud, or risk modeling domains
  • Experience designing and shipping ML- or AI-driven product features
  • Experience applying ML or AI to decision-making systems
  • Experience designing guardrails and human-in-the-loop mechanisms
  • Familiarity with LLMs and agent-based approaches

Work Rights

Not specified

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