Case Study: WorldQuant Predictive achieves faster, safer AI model scaling and 10X broader data access with Immuta

How WorldQuant Predictive Gives Organizations an AI Decision-Making Edge

WorldQuant Predictive (WQP) builds fast, highly accurate AI-driven prediction products by combining thousands of data sources to give organizations a decision-making edge. As it scaled its MLOps pipeline, WQP faced governance and operational challenges—ensuring datasets didn’t contain PII/PHI, restricting validation/test data, granting/revoking access quickly, and maintaining full auditability—so it adopted Immuta’s data access and security platform to manage those risks while keeping speed and accuracy.

Immuta was integrated across WQP’s Databricks and Snowflake stack to automate data checks, masking/anonymization, policy enforcement, and user/group controls, letting teams grant or revoke access in minutes and run quick audits. The result: a simpler architecture, 10× expansion in safe data access, faster dataset onboarding, and more accurate, resilient models that gave customers a competitive edge during COVID-19—benefits WQP attributes directly to Immuta.

WorldQuant Predictive

Slava Frid

Platform Architect & CTO