The core mission of the Data Science organization is to drive algorithmic product innovation across the company and to support client-facing field teams in their reporting, analytics, and story-telling needs. In both aims we ultimately seek to empower our customers through data. Data science product projects underlie major product features for both buyers and sellers on our platform as well as the marketplace design for our ad exchange itself, and it plays a starring role in finding and eliminating fraudulent ad traffic from our platform. Analytics reports and dashboards support clients large and small in all business segments, and provide novel platform-wide insights. On the data product side, our toolkit includes elements of theory – probability models, statistics, machine learning, game theory – as well as practical tools and techniques for manipulating and exploring vast quantities of data. On the analytics side, we leverage many of the same big-data tools and also layer in world-class data visualization. Depending on the exact role, we work closely with production engineers, product managers, operations, account managers, marketing, and sometimes directly with clients to create novel features, systems, and analyses. We love that our contributions further AppNexus’ position as a pioneer and thought-leader in the adtech industry, but at the end of the day, we measure our efforts based on the impact and the value that we create for our clients.
Today the team comprises data scientists and analysts. We have diverse set of backgrounds (including math, physics, statistics, operations research, econometrics, political science) and a spectrum of credentials (about a third have PhDs). Our culture centers on collaborating, continuously learning and teaching, and producing effective solutions, whether they rely on elegant theory, or (occasionally) MacGyver-worthy hacks. We’re also friendly, laid back, and a little eccentric.
AppNexus is aggressively investing in its Data Science function in 2015. In addition to scaling up the team further, we’re looking to build out a brand new Data Science hardware platform strictly for internal research and experimentation, and a small Core Data Science Engineering function to go along with it. Exciting times ahead for Data Science!