Applied Science Manager, Sponsored Products and Brands -- Offsite

Amazon Amazon · Big Tech · NY +1 · Database Administration

Manager for an Applied Science team focused on extending Amazon Ads campaigns beyond the Amazon store using generative AI and machine learning to deliver sponsored experiences in third-party environments. The role involves leading scientists and engineers, shaping product direction, developing roadmaps, and driving advertiser outcomes through ML/GenAI solutions.

What you'd actually do

  1. Lead Applied Scientists, Machine Learning Engineers, and Software Development Engineers.
  2. Surface qualitative and quantitative insights as the voice of the advertisers to shape product direction and ensure product-market fit.
  3. Develop science and engineering roadmaps to increase advertiser outcomes with ML and GenAI solutions, run annual planning, and foster cross-team collaboration on model development.
  4. Hire and develop top talent, provide technical and career development guidance to scientists and engineers within and across the organization.
  5. Work with off-Amazon publishers to launch new high-impact placements that increase advertiser outcomes.

Skills

Required

  • 4+ years of applied research experience
  • 3+ years of scientists or machine learning engineers management experience
  • 3+ years of building machine learning models for business application experience
  • PhD, or Master's degree and 6+ years of applied research experience
  • Knowledge of ML, NLP, Information Retrieval and Analytics
  • Experience programming in Java, C++, Python or related language

Nice to have

  • Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
  • Experience in practical work applying ML to solve complex problems for large scale applications
  • Experience working with big data, machine learning and predictive modeling
  • Experience in people management
  • Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.

What the JD emphasized

  • strong background in ML and GenAI solutions
  • building machine learning models for business application
  • deep learning, machine learning and computer vision

Other signals

  • Generative AI
  • Machine Learning
  • Large-scale, low-latency systems