Applied Scientist, Amazon Ads, Demand Forecasting & Guidance

Amazon Amazon · Big Tech · Palo Alto, CA · Applied Science

Applied Scientist role at Amazon Ads focusing on Generative AI, AI Agents, and large-scale ML for demand forecasting. Responsibilities include leading ML initiatives, developing and optimizing forecasting models, building and deploying ML models, designing A/B experiments, establishing scalable ML infrastructure, and researching innovative ML techniques. Requires 3+ years of model building experience, a PhD or Master's with 4+ years of experience in a related field, and programming experience in Java, C++, or Python. Preferred qualifications include experience with experimental design and deep learning frameworks.

What you'd actually do

  1. Lead and contribute to end-to-end ML initiatives with high ambiguity, scale, and complexity from problem formulation through production deployment
  2. Develop and optimize forecasting models by performing hands-on analysis of large-scale datasets to improve ad delivery prediction accuracy and operational efficiency
  3. Build, experiment, and deploy machine learning models through rapid prototyping, rigorous experimentation, and close collaboration with software engineering teams for seamless productization.
  4. Design and execute A/B experiments, collect performance data, and conduct statistical analysis to validate model impact
  5. Establish scalable ML infrastructure including automated pipelines for data processing, model training, validation, and serving

Skills

Required

  • 3+ years of building models for business application experience
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Nice to have

  • Experience in designing experiments and statistical analysis of results
  • Experience with popular deep learning frameworks such as MxNet and Tensor Flow

What the JD emphasized

  • building models for business application experience

Other signals

  • Generative AI
  • AI Agents
  • large-scale ML
  • forecasting models
  • ML infrastructure