Consumer Airfare Prediction and other Big Data AI Challenges at Hopper
At Hopper, we help consumers make smarter decisions about booking their air & hotel at the right time for the right price. This session is a deep dive into airfare pricing, including data visualization, how we enable millions of personalized conversations with our users focused on travel flexibility and alternative suggestions, building trust with consumers around data, our price prediction algorithms, tackling big data AI challenges in a low-frequency industry and other problems that keep us up at night.
Patrick leads data science at Hopper, extracting insight and generating value from the large volumes of travel data that Hopper collects. He is a leading global analytics practitioner with a wealth of experience in real-world delivery of customer insight, predictive analytics and behavior modeling, as well as a pioneering innovator in uplift modeling research.
He was one of the founders of Quadstone Inc, a predictive customer analytics company, since acquired by Pitney Bowes Software, where he developed high-performance parallel engines for generalized additive models. Patrick holds a PhD in mathematics and statistics from the University of Edinburgh, where he studied optimization based on evolutionary algorithms, following an HBSc in continuum mechanics from the University of Western Ontario.