Sr. Applied Scientist, MAPLE

Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art recommendation system modeling and GenAI techniques? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science by publishing research!

Key job responsibilities

• Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization.
• Develop and test new signals for improving recommendation models
• Use supervised and uplift learning algorithms to improve customer experience
• Contribute to production code and science tooling
• Design A/B tests and conduct statistical analysis on their results
• Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers
• Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area
• Present and publish science research internally and externally, contributing to Amazon's science community
• Mentor junior engineers and scientists.

About the team

Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers.

Basic qualifications

• 6+ years of building machine learning models for business application experience
• PhD, or Master's degree
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Preferred qualifications