Justin Norman

Vice President Data Science & Analytics
Yelp

Justin is currently a Vice President and the head of Data Science at Yelp. He is a career data professional and Data Science leader with experience in multiple industries and companies. Previously, Justin was the Director of Research and Data Science at Cloudera Fast Forward Labs, head of Applied Machine Learning at Fitbit, the head of Cisco’s Enterprise Data Science Office and a Big Data Systems Engineer with Booz Allen Hamilton. In another life, Justin served as a Marine Corps Officer, with a focus in Systems Analytics and Device Intelligence. Justin is a graduate of the US Naval Academy with a degree in Computer Science and the University of Southern California with a Master’s Degree in Business Administration and Business Analytics.

Building the Business Case for ML Platforms
Friday, January 22 | 
09:45 AM - 
10:15 AM

ML projects are expensive. For business leaders considering where the next incremental investment in ML goes, there is a natural tension between investing in front line ML projects and the platforms, tools and teams that can accelerate these projects and help ensure their success. In this session we discuss how to explain the value of ML platforms and infrastructure investments and build the business case for them.

ML Product Experiments at Scale
Wednesday, January 27 | 
11:10 AM - 
11:40 AM

Today, nearly all data experimentation at Yelp—from products to AI and machine learning—occurs on the custom-built Bunsen platform, with over 700 experiments in total being run at any one time. Bunsen supports the deployment of experiments to large but segmented parts of Yelp’s customer population, and it enables the company’s data scientists to roll back these experiments if need be.

However, adapting a digital product A/B testing system to support complex ML-powered use cases required advanced techniques, highly cross-functional product, engineering and ML teamwork and a unique design approach. This talk will explore lessons learned and best practices for building robust experimentation workflows into production machine learning deployments.

Scroll to Top