Applied Scientist, Sales AI

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

This role focuses on building AI/ML solutions for the Ad Sales business, specifically creating customer-facing recommendations and enhancing end-to-end workflows with Generative AI. The scientist will leverage quantitative modeling techniques like Sequential Recommender Systems, Deep Learning, and Reinforcement Learning, and use NLP and Generative AI for explainability. The role involves research, model development, A/B testing, and collaboration with engineering and product teams to deliver production-ready solutions.

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

  • 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 in patents or publications at top-tier peer-reviewed conferences or journals
  • 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 using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • state-of-the-art research
  • customer-facing recommendations
  • Sequential Recommender Systems
  • Deep Learning
  • Reinforcement Learning
  • Natural Language Processing
  • Generative AI
  • latest Generative AI systems and services
  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • building new, science-backed services
  • actionable insights and recommendations
  • ingesting Generative AI throughout their end-to-end workflows
  • customer-facing recommendations
  • Sequential Recommender Systems, Deep Learning, Reinforcement Learning or Hidden Markov Models
  • Natural Language Processing and Generative AI models to enhance their explainability
  • latest Generative AI systems and services to accelerate and improve your work