Applied Scientist, Pricing and Promotion Optimization

Amazon's Pricing & Promotions Optimization Science is seeking a motivated Applied Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to regularly generate fresh customer-relevant prices and promotions on billions of Amazon and Third Party Seller products worldwide.

We are looking for a talented, organized, and customer-focused applied scientists to define, measure, and launch customer-obsessed solutions across all products listed on Amazon.

This role requires an individual with exceptional AI and data science expertise, excellent cross-functional collaboration skills, strong 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/promotion techniques

- Build strong collaborations. Partner with product, engineering, and science teams within and outside Pricing & Promotions org to deploy AI/ML 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, reinforcement learning, 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 science and tech problems


About the team
About the team: the Pricing and Promotion Optimization team within P2 Science leads the definition, measurement, and implementation of the state-of-the-art AI and data science solutions to improve price/promotion quality across the site and bring value to customers, sellers and Amazon. - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- 2+ years of hands-on predictive modeling and large data analysis experience- Experience building machine learning models or developing algorithms for business application
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Reinforcement Learning and Optimization systems

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, 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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary for this position ranges from $149,300/year up to $249,300/year. Salary is based on a number of factors 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. Applicants should apply via our internal or external career site.