Sr. Applied Scientist, Pricing Science

We are looking for a talented, organized, and customer-focused applied researcher 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 exceptional machine learning modeling and architecture background, excellent cross‑functional collaboration skills, business acumen, and an entrepreneurial spirit. It ideally suits a self‑starter comfortable with ambiguity, who pays close attention to detail and thrives in a fast‑paced, 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—keep up to date with the latest scientific advancements in machine learning, neural networks, NLP, probabilistic forecasting, and multi‑objective optimization, and identify opportunities to apply them to relevant Pricing & Promotions business problems.
• Foster continuous learning—promote rapid experimentation, continuous learning, and incremental value delivery.
• Apply technical expertise—to incrementally move the needle on some of our toughest pricing problems.

A Day in the Life

We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. Responsibilities include:

• Shape and extend our RL optimization platform—a pricing‑centric tool that automates the optimization of system parameters and price inputs.
• Develop error‑detection and price‑quality guardrails at scale.
• Identify opportunities to optimally price across systems and contexts (marketplaces, request types, event periods).

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

Basic Qualifications

• 4+ years of applied research experience.
• 3+ years of building machine learning models for business applications.
• PhD or Master’s degree and 6+ years of applied research experience.
• Experience programming in Java, C++, Python, or related language.
• Experience with neural deep learning methods and machine learning.

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, etc.
• Experience with large‑scale distributed systems such as Hadoop, Spark, etc.

Amazon is an equal‑opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several U.S. geographic markets. The base pay for this position ranges from $150,400 per year in our lowest geographic market up to $260,000 per year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job‑related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign‑on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.