Applied Scientist, Sales AI

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

Applied Scientist role focused on building and refining Generative AI and ML models to optimize Amazon's Ad Sales business. The role involves conceptualizing research, guiding technical approaches, conducting data analysis, running A/B experiments, and working with engineers to deliver end-to-end solutions into production. The goal is to transform account team operations with actionable insights, recommendations, and GenAI integration for improved efficiency.

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

  • generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF
  • implementing algorithms using both toolkits and self-developed code
  • applying theoretical models in an applied environment
  • designing experiments and statistical analysis of results

What the JD emphasized

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

Other signals

  • building new, science-backed services
  • transform the way account teams operate
  • creating actionable insights and recommendations
  • ingesting Generative AI throughout their end-to-end workflows
  • build and refine models that can be implemented in production
  • improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving