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Scaling Quantitative Research on Sensitive Data

  • Slava Frid Platform Architect & CTO, Worldquant

WorldQuant Predictive is a data science company that leverages proven machine learning, AI, and quantitative finance approaches to solve new business challenges. Their primary business objective is to enable customers to get to prediction and insights faster, while reducing the barrier of entry from a cost and talent perspective. To deliver on this, Worldquant Predictive’s data platform requires scaling analytics workflow across a pre-ingested data catalog of thousands of sources and a pre-built catalog for hundreds of models and having a global research team constantly engaged in finding new models, data sources, and approaches for modeling business decisions. Managing this data at scale is a challenge. It’s critical to ensure that confidential information is protected by data access controls that maximize the number of hands and minimize the number of eyes working on a specific prediction product. Further, it’s necessary to provide transparency and trust through detailed audits of the data use for customers.

This recorded presentation discusses the approaches WorldQuant Predictive took to scale their quantitative research in a complex, sensitive data environment with Immuta’s data access and security platform.