Applied Scientist, Sales AI

Amazon Amazon · Big Tech · CA, ON +1 · Applied Science

Applied Scientist role focused on Generative AI and quantitative modeling for Amazon Advertising Sales. The role involves conceptualizing and leading research on ML/GenAI solutions, guiding technical approaches, conducting data analysis, running A/B experiments, and working with engineers to deliver end-to-end solutions into production. Key areas include optimizing sales business, improving work efficiency through GenAI, and developing advertiser insights and recommendations.

What you'd actually do

  1. Conceptualize and lead state-of-the-art research on new Machine Learning and Generative Artificial Intelligence solutions to optimize all aspects of the Ad Sales business
  2. Guide the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects
  3. Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities
  4. Run regular A/B experiments, gather data, and perform statistical analysis
  5. Work closely with software engineers to deliver end-to-end solutions into production
  6. Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving

Skills

Required

  • building models for business application
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • patents or publications at top-tier peer-reviewed conferences or journals
  • programming in Java, C++, Python or related language
  • algorithms and data structures
  • parsing
  • numerical optimization
  • data mining
  • parallel and distributed computing
  • high-performance computing

Nice to have

  • Unix/Linux
  • professional software development

What the JD emphasized

  • building models for business application experience
  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • Generative AI
  • quantitative modeling
  • forecasting
  • recommender systems
  • reinforcement learning
  • causal inferencing
  • production models
  • A/B experiments
  • large-scale data analytics
  • model training
  • deployment
  • serving