Slava is a 20-year+ technology industry veteran focusing on solving business problems with Software Engineering, He has led award-winning teams in finance, media, and non-profit sectors and enjoys pushing the envelope of what is considered possible. He is currently embracing the challenge of helping to build a new way to enable Quantitative Researchers to answer toughest questions businesses and organizations have. He received his degree in Economics from the Wharton School and his Computer Science degree from the School of Engineering at the University of Pennsylvania.
Worldquant Predictive is a data science company that leverages proven machine learning, AI, and quantitative finance approaches to solve new business challenges across a variety of industries such as healthcare, retail, CPG, and others. The 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, our data platform requires scaling the 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 that only grows when you add words, “safely and confidentially.” It’s critical to ensure that confidential data is protected with automated 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. Protecting data access across this environment has become a critical bottleneck, and this session will discuss the approaches we are taking to scale our quantitative research efforts in this complex, sensitive data environment.