Abstract: 

With many organizations now relying on a multitude of data sources to inform their decision-making, the expectation has now been set on stakeholders and leaders to depend on, and act on, a perpetually growing technology stack and volume of data. Despite the competitive advantage that clean data can bring to an organization, there also exists a real risk of drowning in an abundance of interesting, but ultimately unimpactful, data and insights. In this talk, we will go through two detailed use-cases of how Getty Images combined Data Science and business needs to build a comprehensive suite of data-based frameworks and products to track, monitor and alert on e-commerce performance, and to help understand how customers consume our vast collection of imagery content.

Bio: 

As Head of Data Science at Getty Images, Thomas Vincent utilizes data to optimize Getty Images' understanding of the customer lifecycle and leads the organization in the use of data science and engineering across the business. An experienced statistician and data scientist, Thomas works with Getty Images' Global Demand Generation, Digital Marketing and Sales teams to help maximize revenue, and ensure that both internal and third-party data are leveraged in the most appropriate and efficient way.
Prior to this role, Thomas worked as a Senior Data Science Engineer & Technical lead at DigitalOcean where he was instrumental in overseeing a variety of machine learning-based projects focused on Customer Service, Fraud Detection and Marketing/Sales. Thomas also worked as a Data Scientist at Dow Jones, where he designed customer-centric machine learning pipelines and created a customized search algorithm as a product to replace third-party tools. Thomas obtained a Ph.D. in Biostatistics from the University of Bristol, which was preceded by an MSci in Mathematics & Physics and an MRes in Complexity Sciences.