About the job
About DoubleVerify and Scibids
DoubleVerify is a pioneering force in the realm of AI-driven digital campaign activation through its innovative subsidiary, Scibids. By leveraging advanced technology, Scibids enhances the advertising performance of global brands while streamlining operational processes and minimizing manual labor. Our solutions are designed to function without relying on digital identifiers such as cookies and can be seamlessly integrated across top Demand-Side Platforms, including The Trade Desk, DV360, and Xandr. For further insights, visit https://doubleverify.com/scibids-ai/
Scibids' AI Optimization technology empowers trading desks with state-of-the-art machine learning capabilities, utilizing real-time performance data to identify optimal bidding strategies for each advertising opportunity, resulting in significant performance improvements for advertisers. Our dedicated team of over 20 Data Scientists, located in our Paris office, collaborates with DoubleVerify's global engineering and data science teams.
About the Role
The primary objective of Scibids at DoubleVerify is to equip media buyers with fully automated purchasing strategies for display campaigns, achieving unparalleled performance with minimal human intervention.
As a Data Scientist, your responsibilities will include evaluating and enhancing the bidding algorithms of Scibids DoubleVerify, the organization’s core asset, and actively contributing to the automation and development processes within Scibids. Each business case that is not yet automated should be addressed with corresponding product enhancements. This position will require a blend of analytical rigor, insatiable curiosity, solid knowledge of statistical methodologies, and practical experience in implementing these techniques.
Your day-to-day tasks will involve:
- Analyzing massive datasets (billions of ad impressions per month) containing millions of variables.
- Addressing complex analysis challenges that cannot be solved with standard machine learning solutions.
- Overcoming scaling and engineering challenges as our system generates thousands of machine learning models daily.
- Collaborating closely with commercial teams and other stakeholders to ensure alignment with business goals.