We are currently looking for a Data Scientist to support the ongoing development of MediaMath’s proprietary algorithms and analytics. This individual will be a member of the Data Science team, working closely with the Product & Engineering teams on the conception, design, development, testing, and deployment of real-world applications of models & data that impact billions of dollars of marketing spend. From optimizing real-time bidding auctions, to separating human from non-human web traffic, to building out a global cross-device graph across billions of users, this individual will have the opportunity to work on numerous cutting-edge problems and develop scalable, high-performance solutions to big data problems using state-of-the art technologies, languages and frameworks.
What you’ll do:
Design and develop Machine Learning models and algorithms that drive performance and provide insights, from prototyping to production deployment, across key areas of interest to the company (e.g., bidding optimization, messaging optimization, multi-touch attribution, fraud detection, device identification, cross-device association)
Partner closely with Engineering on the architecture and implementation of modeling efforts to ensure performance and scalability
Develop tools and processes to monitor performance of existing models and implement enhancements to improve scalability, reliability, and performance.
What you’ll need:
We are building a new accountable & addressable supply chain for the industry. Brands and their partners use our technology and services to achieve the performance they deserve with less effort. We created the first software for real-time media buying in 2007 and today work with over one-third of the Fortune 500 and more than 3,500 brands and their agency partners to grow and deepen direct customer relationships. Our clients can access the purest supply to reach real humans at the scale they need across channels like mobile, CTV and display and can use a combination of advanced AI and human expertise to drive improved results over time.
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