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Description

We are looking for a talented, organized, and customer-focused applied researchers to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon.

This role requires an individual with exceptional machine learning modeling and architecture expertise, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit.

We are looking for an experienced innovator, who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment.


Key job responsibilities
* See the big picture. Understand and influence the long term vision for Amazon's science-based competitive, perception-preserving pricing techniques
* Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale
* Stay informed. Establish mechanisms to stay up to date on latest scientific advancements in machine learning, neural networks, natural language processing, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems
* Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery.
* Successfully execute & deliver. Apply your exceptional technical machine learning expertise to incrementally move the needle on some of our hardest pricing problems.

A day in the life
We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through:

* shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs.
* Error detection and price quality guardrails at scale.
* Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods)

Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication.