Applied Scientist, Sales AI

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

Applied Scientist role focused on building AI/ML solutions for Amazon's Advertising Sales business. The role involves developing and implementing models for insights, recommendations, and generative AI-powered workflows to improve sales team efficiency and customer success. It requires expertise in quantitative modeling, deep learning, RL, and NLP, with a focus on production deployment and A/B experimentation.

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. Lead 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. Run regular A/B experiments, gather data, and perform statistical analysis
  4. Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving
  5. Partner with software engineering and product management teams to support product and service development, define success metrics and measurement approaches, and help drive adoption of innovative new features for our services.

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
  • modeling frameworks that support sequential decision making such as Hidden Markov Models, Sequential Recommender Systems and Reinforcement Learning

What the JD emphasized

  • state-of-the-art research
  • Machine Learning
  • Generative Artificial Intelligence
  • models and algorithms
  • A/B experiments
  • data analytics
  • model training
  • deployment
  • serving
  • product and service development
  • innovative new features
  • quantitative modeling techniques
  • Deep Learning
  • Reinforcement Learning
  • Hidden Markov Models
  • Natural Language Processing
  • Generative AI models
  • latest Generative AI systems and services

Other signals

  • Generative AI
  • recommendations
  • insights
  • workflows