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 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.

• 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 the 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.

• 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 store architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication.

• 4+ years of applied research experience
• 3+ years of building machine learning models for business application experience
• 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

• Experience with modeling tools such as R, scikit‑learn, Spark MLLib, MxNet, Tensorflow, Num Py, Sci Py 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 (Use the "Apply for this Job" box below). 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 US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/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.