Applied Scientist, Prime Tech, Prime Science - ML

Interested in helping build Prime's content and offer personalization system to drive huge business impact on millions of customers? Join our team of Scientists and Engineers developing algorithms to adaptively generate, optimize, and personalize the customer experience with Amazon Prime. This includes identifying who our customers are and providing them with personalized relevant content. As an ML scientist, you will partner directly with product owners to intake, build, and directly apply your modeling solutions.
There are numerous scientific and technical challenges you will get to tackle in this role, such as deep learning and reinforcement learning, and their application to various types of contextual, multi-step optimization of the customer journey. We employ techniques from supervised learning, multi-armed bandits, optimization, and RL - while this role is focused on the space of discriminative and generative recommender systems.
As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.
You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Redshift, Sagemaker, DynamoDB, S3, ...), various ML algorithms and techniques (Random Forests, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning, LLMs), and statistical modeling techniques.

Major responsibilities

- Build and develop machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams.

- Leverage Bandits, Supervised Learning, and Reinforcement Learning for Contextual Recommendation and Optimization Systems.

- Develop offline policy estimation tools and integrate with reporting systems.

- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.

- Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.

- Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.

- Use machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.

- Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.