Senior Applied Scientist, Sponsored Products Bidding

Amazon Amazon · Big Tech · Palo Alto, CA · Machine Learning Science

Senior Applied Scientist role focused on AI/ML for Sponsored Products Bidding at Amazon Advertising. The role involves leading new initiatives, performing hands-on analysis and modeling, driving end-to-end ML projects from development to production deployment, and researching new AI/ML approaches. It requires building and deploying models, running A/B experiments, and establishing scalable processes for ML development and serving. The role also involves technical leadership, mentorship, and contributing to the long-term science vision for the advertising business.

What you'd actually do

  1. Lead a new initiative across Sponsored Products Bidding focused on AI/ML based features.
  2. Be the technical leader in AI, Machine Learning; lead efforts within this team and across other teams.
  3. Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.
  4. Drive end-to-end AI/Machine Learning projects that have a high degree of ambiguity, scale, complexity.
  5. Build models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your AI/ML models.

Skills

Required

  • AI/ML
  • Machine Learning
  • Data Analysis
  • Model Development
  • Model Deployment
  • A/B Testing
  • Statistical Analysis
  • Software Engineering collaboration

Nice to have

  • Science Leadership
  • Organizational Ability
  • Technical Strength
  • Product Focus
  • Business Understanding
  • Mentorship
  • Recruiting

What the JD emphasized

  • AI/ML based features
  • technical leader in AI, Machine Learning
  • end-to-end AI/Machine Learning projects
  • deploy your models into production
  • AI/ machine learning approaches

Other signals

  • AI/ML based features
  • end-to-end AI/Machine Learning projects
  • models into production
  • large-scale data analysis
  • machine-learning model development
  • model validation and serving
  • innovative AI/ machine learning approaches