Applied Scientist , Pae

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

Applied Scientist role focused on building and shipping ML systems for Amazon's Payment Acceptance & Experience (PAE) team. The role involves designing, developing, evaluating, deploying, and monitoring ML models that personalize payment experiences for over 300 million customers and 2 billion monthly transactions. Key responsibilities include solving global problems with scalable models, owning the full ML lifecycle from experimentation to production inference, measuring impact through A/B testing and statistical analysis, collaborating with engineers and product managers, and advancing the science through internal/external publications. The role operates at a high volume, low latency decision system, directly impacting revenue and customer experience.

What you'd actually do

  1. Build and ship ML systems at scale — design, develop, evaluate, deploy, and monitor ML models that personalize payment experiences for 300MM+ customers across 2B+ monthly transactions.
  2. Solve global problems once — develop worldwide models that scale across business lines and locales with minimal adaptation, and continuously improve model performance and ML architecture.
  3. Own the full lifecycle — contribute production-grade code and science tooling, from experimentation framework to deployed inference.
  4. Measure real impact — design A/B experiments, conduct rigorous statistical analysis, and translate results into product and business decisions.
  5. Ship with engineering and product partners — collaborate with SDEs to take models from prototype to production, and with business stakeholders to drive alignment on science-informed strategy.

Skills

Required

  • Experience programming in Java, C++, Python or related language
  • Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
  • Currently has, or is in the process of obtaining, a Master's degree or above in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields

Nice to have

  • Experience implementing algorithms using both toolkits and self-developed code
  • Have publications at top-tier peer-reviewed conferences or journals
  • Currently has, or is in the process of obtaining, a PhD in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
  • Experience building machine learning models or developing algorithms for business application

What the JD emphasized

  • build the intelligence behind it
  • applying machine learning, GenAI, and real-time personalization
  • highest-volume, lowest-latency decision systems
  • build models that directly move billions in revenue
  • predicting payment risk before it happens
  • recommending the right payment method at the right moment
  • eliminating friction that customers shouldn't have to think about
  • build the ML systems that power Amazon's Payment Experience Intelligence
  • shipping at a scale few teams in the industry can match
  • production-grade code
  • advance the science
  • publications at top-tier peer-reviewed conferences or journals

Other signals

  • ML systems at scale
  • personalize payment experiences
  • move billions in revenue
  • predicting payment risk
  • recommending the right payment method
  • eliminating friction