โ† Back to jobs

Senior Applied Scientist , Channel Growth

The Trade Desk
INTERN Remote ยท US Ventura, CA, US Posted: 2026-05-11 Until: 2026-06-10
Apply Now โ†’
You will be redirected to the original job posting on BeBee.
Apply directly with the employer.
Job Description
The Trade Desk is a global technology company and the world's leading independent platform for digital advertising, with nearly 4,000 employees across more than 30 offices. Our technology helps advertisers reach the right audiences across the open internet - from streaming TV and podcasts to mobile apps, news, and more. Advertising powers the content people love. By making it more transparent, effective, and responsible, we help support trusted journalism, quality entertainment, and creators worldwide. The world's brands and agencies rely on us to reach their customers and grow their businesses responsibly. The scale of our platform brings unique technical challenges - from processing massive datasets in real time to building systems that operate reliably on a global scale. When you work here, your impact is worldwide. We welcome diverse perspectives, encourage curiosity, and build teams that learn from one another. If you're driven to solve meaningful challenges, we'd love to meet you. Data scientists at TTD work closely with engineering throughout the lifecycle of the product, from ideation to productionization and monitoring. Our data scientists are end-to-end owners. You will participate actively in all aspects of designing, researching, building, and delivering data-focused products for our clients and traders. We are looking for a Senior Data Scientist to design, build, and scale data driven solutions powering our forecasting, pacing, and recommendation systems for our emerging channels . You will develop advanced machine learning or deep learning models that directly influence planning, allocation and performance on our platform. The main job directions include: Develop forecasting models using statistical, ML and deep learning approaches. Build and optimize pacing algorithms that balance short-term performance with