Chris joined Databricks in July of 2019 as a Solutions Architect. Previously, as Director of Data Science for Digital Marketing and Fraud Prevention at Overstock, he and his team utilized big data and machine learning to create personalized shopping experiences for customers. With a focus on working to unite data flow and Data Science to facilitate an omni-channel marketing experience driven by real-time data, he worked extensively on display, email, PLA, and Paid Keywords leveraging modeling techniques from classical time series to cutting-edge Artificial Intelligence. Prior to joining Overstock in 2016, Chris gained widespread experience at early-stage startups using Apache Spark and building out data science frameworks and solutions. He graduated from the University of Utah with dual Masters Degrees in Computer Science and Statistics.
May 26, 2021 11:30 AM PT
We will share our experiences in building Data Science and Machine Learning (DS/ML) into organizations. As new DS/ML teams are created, many wrestle with questions such as: How can we most efficiently achieve short-term goals while planning for scale and production long-term? How should DS/ML be incorporated into a company?
We will bring unique perspectives: one as a previous Databricks customer leading a DS team, one as the second ML engineer at Databricks, and both as current Solutions Architects guiding customers through their DS/ML journeys.We will cover best practices through the crawl-walk-run journey of DS/ML: how to immediately become more productive with an initial team, how to scale and move towards production when needed, and how to integrate effectively with the broader organization.
This talk is meant for technical leaders who are building new DS/ML teams or helping to spread DS/ML practices across their organizations. Technology discussion will focus on Databricks, but the lessons apply to any tech platforms in this space.
June 5, 2018 05:00 PM PT
How Data Scientists and Engineers work in tandem to achieve real-time personalization at Overstock
Personalizing online experiences for users is nothing new, but real-time personalization requires sub-second speed and close collaboration between data scientists and enterprise engineers.
Like the hands on a clock, data scientists and enterprise engineers have shifted their focus from hour- hand quickness to minute-hand speeds with a craving to take advantage of each tick of the second hand and personalize in real-time. Previously, daily activities were consumed on improving customers’ experiences tomorrow. Workflows ran overnight when on perm resources were not being tasked. The focus was on the-day-before jobs, always inching forward 24-hours behind.
Since then, we have shifted to hourly jobs and even to tasks that run every five minutes. Finally, we have been personalizing user experiences within the same day and even during the same session. But could we personalize these experiences instantly, immediately, and in real-time? What would that require? What does it look like? Michael Finger and Chris Robinson explore how data scientists and engineers are working in tandem to achieve real-time personalization at Overstock.com
Session hashtag: #EntSAIS10
June 5, 2018 05:00 PM PT
With great data, comes great responsibility. At Overstock.com, lack of data has never been an issue. We know everything from the color you search most, to which room you’ll redesign next. We can see individuals transition from furnishing their first flat to building their dream home, but processing this data requires some serious firepower. It has fueled our focus on delivering real-time personalization through the unification of data and AI. Databricks is at the crux of this vision – empowering us to leverage cloud-scale with a platform that simplifies data engineering and increases the productivity of our data science team.
Tune in as Chris Robison takes you through marketecture innovations in building a successful marketing technology infrastructure for instantaneous individualized marketing experiences.