Integral Ad Science (IAS) is a leading global media measurement and optimization platform that delivers the industry’s most actionable data to drive superior results for the world’s largest advertisers, publishers, and media platforms. IAS’s software provides comprehensive and enriched data that ensures ads are seen by real people in safe and suitable environments, while improving return on ad spend for advertisers and yield for publishers. Our mission is to be the global benchmark for trust and transparency in digital media quality. For more information, visit integralads.com.
As a Data Scientist/MLE at IAS, you will be part of a team that is a center of innovation for the company and a major contributor to the company’s core products. The Principal Data Scientist will work on challenging technical problems in brand safety, developing and pushing out ML/DL models at scale that classify brand unsafe material for multiple modalities across the digital ecosystem. The types of challenges we solve have attracted people from industry and academia with diverse backgrounds, including machine learning, computer science, finance, physics, biology, neuroscience, and economics. We’re passionate about maintaining an open and collaborative environment, where team members bring their own unique style of thinking and tools to the table.
The ideal candidate has a track record of delivering deep learning based models at scale, enjoys working with cross functional partners from Product and Engineering delivering solutions within strict timelines, and brings innovative solutions to complex problems with a desire to improve the status quo. Additionally, as an early member of our Dublin location, you will also have the opportunity to help build the team from the ground up and shape the office culture.

• Scope work and projects in the realm of machine learning, focusing on deep learning applications in image/video classification.
• Work with a variety of data sources to build production-ready ML models
• Apply machine learning to novel problems in online advertising
• Collaborate with engineers to integrate solutions within larger engineering workflows
• Effectively communicate results from ML/DL models and statistical learnings to a variety of stakeholders within R&D and product organizations