Amazon

DESCRIPTION

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 define and optimize customers-obsessed pricing and promotions strategies on billions of Amazon and Third Party Seller products worldwide.

This role requires an individual with exceptional AI and data science expertise in domains such as ranking, multi-objective optimization, causal inference, time-series forecasting, and reinforcement learning.

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/Data Science solutions at Amazon scale
• Stay informed. Establish mechanisms to stay up to date on latest scientific advancements in neural networks, search & ranking, 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 AI and data science 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 Pricing and Promotion (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.

BASIC QUALIFICATIONS

• 5+ years of building machine learning models for business application experience
• PhD, or Master's degree and 5+ 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 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, protected veteran status, 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.

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 https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.