Staff Applied Scientist, Recommender Systems

The Trade Desk-posted 28 days ago
Full-time • Mid Level
New York, NY
1,001-5,000 employees
Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services
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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.

  • 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.
  • comprehensive healthcare (medical, dental, and vision) with premiums paid in full for employees and dependents
  • retirement benefits such as a 401k plan and company match
  • short and long-term disability coverage
  • basic life insurance
  • well-being benefits
  • reimbursement for certain tuition expenses
  • parental leave
  • sick time of 1 hour per 30 hours worked
  • vacation time for full-time employees up to 120 hours thru the first year and 160 hours thereafter
  • around 13 paid holidays per year
  • Employees can also purchase The Trade Desk stock at a discount through The Trade Desk's Employee Stock Purchase Plan.
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