Staff Applied Scientist, Recommender Systems
- 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.
- 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
- Experience in deep learning, TensorFlow/PyTorch a bonus.
- The ability to communicate with diverse stakeholders, making architecture recommendations, ensuring effective execution, and measuring quality of outcomes.
- Experience running heavy workloads on a distributed computing cluster (especially EMR or Databricks), leveraging technologies like Spark to work with large datasets preferred.
- Proficient in Python. Strong spark skills are a plus.
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