Applied Scientist, Recommender Systems

The Trade Desk is a global technology company with a mission to create a better, more open internet for everyone through principled, intelligent advertising. Handling over 1 trillion queries per day, our platform operates at an unprecedented scale. We have also built something even stronger and more valuable: an award‑winning culture based on trust, ownership, empathy, and collaboration. We value the unique experiences and perspectives that each person brings to The Trade Desk, and we are committed to fostering inclusive spaces where everyone can bring their authentic selves to work every day.

Do you have a passion for solving hard problems at scale? Are you eager to join a dynamic, globally connected team where your contributions will make a meaningful difference in building a better media ecosystem? Come and see why Fortune magazine consistently ranks The Trade Desk among the best small‑medium‑sized workplaces globally.

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.

This particular role focuses on developing forecasting and recommendation models that power intelligent planning, pacing, and optimization across the platform. You will build systems that help advertisers make data‑informed decisions on audience selection, inventory allocation, and bidding strategy. Accurate forecasting and recommendation are essential for giving real‑time feedback to our clients, improving campaign outcomes, and driving media efficiency at scale.

The main job directions include:

• Design and build large‑scale recommendation systems that guide advertisers and traders toward optimal campaign setups — including audience selection, inventory mix, and bidding strategies.
• Develop data‑driven recommendation models that leverage historical campaign performance, marketplace dynamics, and user history to surface intelligent suggestions in real time.
• Collaborate with product and engineering teams to integrate recommendation engines into planning, optimization, and reporting tools across The Trade Desk platform.
• Build and maintain robust feature pipelines and ranking models that improve recommendation accuracy, diversity, and interpretability.
• Partner with downstream teams to define success metrics and design experimentation frameworks (e.g., A/B testing) to evaluate model impact on client and platform performance.
• Continuously analyze campaign and marketplace data to identify opportunities for new or improved recommendation products
  , using user feedback and model diagnostics to drive iteration.
• Ensure recommendations are privacy‑safe, scalable, and explainable
  , aligning with TTD’s principles of transparency and trust.

• Strong foundation in machine learning and deep learning, with experience developing and deploying recommendation or ranking systems.
• Solid understanding of forecasting and predictive modeling, especially in dynamic, large‑scale environments such as ad tech, e‑commerce, or digital media.
• Passion for translating model insights into practical recommendations that improve advertiser outcomes.
• Experience collaborating cross‑functionally with product, engineering, and analytics to ship high‑impact data products.
• Possesses a keen sense of data intuition and the ability to innovate in the field of model development. Has a data driven mindset and uses data to drive your model development plan.

We do not expect you to know every technology we use when you start t we care most about is that you can learn quickly and solve complex problems using the best tools for the job. However, we find that the most successful candidates typically come in with something like the following experience:

• BS/MS with 6+ years or a PhD with 4+ years of experience working in a DS or ML role that involves bringing products from ideation to production.
• Experience working with LLMs, prompt engineering preferred.
• Exp…