Senior Applied Scientist, Sponsored Products and Brands

Amazon Amazon · Big Tech · NY +1 · Applied Science

Senior Applied Scientist role focused on developing and launching AI solutions for Amazon's Sponsored Products and Brands advertising business. This involves building recommendation systems leveraging generative models, designing and implementing end-to-end AI solutions, and defining a long-term science vision. The role requires experience in building ML models, deep learning, and working with large-scale distributed systems.

What you'd actually do

  1. Develop AI solutions for Sponsored Brands advertiser and shopper experiences. Build recommendation systems that leverage generative models to develop and improve campaigns.
  2. You invent and design new solutions for scientifically-complex problem areas and/or opportunities in new business initiatives.
  3. You drive or heavily influence the design of scientifically-complex software solutions or systems, for which you personally write significant parts of the critical scientific novelty. You take ownership of these components, providing a system-wide view and design guidance. These systems or solutions can be brand new or evolve from existing ones.
  4. Define a long-term science vision and roadmap for our Sponsored Brands advertising business, driven from our customers' needs, translating that direction into specific plans for applied scientists and engineering teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.
  5. Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end;

Skills

Required

  • building machine learning models
  • deep learning
  • neural networks
  • Java
  • C++
  • Python

Nice to have

  • R
  • scikit-learn
  • Spark MLLib
  • MxNet
  • Tensorflow
  • numpy
  • scipy
  • large scale distributed systems
  • Hadoop
  • Spark

What the JD emphasized

  • building machine learning models for business application experience
  • neural deep learning methods and machine learning

Other signals

  • generative AI technologies
  • recommendation systems
  • large scale distributed systems