David's experience involves R&D in government and industry, relating to spatiotemporal forecasting, quantitative finance, personalised medicine, natural language processing, information retrieval, and other applications of statistics, especially machine learning. He aims to deliver full solutions: the algorithms, platforms, and teams needed to tackle hard commercial problems.
Most recently, David architected Uber's Machine Learning Platform and its real-time spatiotemporal forecasting platform. These are the basis of applications that add hundreds of millions of dollars to our annual revenue. Overall, he has led the architecture of 5 large scale platforms for machine learning, spatiotemporal forecasting, and time series prediction. These have been very robust, high throughput / low latency, easy to use systems that substantially improved the pace and scale of innovation by data scientists & quants.