About the job
Amazon's Pricing & Promotions Science is seeking a driven 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 on billions of Amazon and Third Party Seller products worldwide. We are looking for a talented, organized, and customer-focused applied researchers to join our Pricing and Promotions 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.
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.
Qualifications
Minimum
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
Preferred
Experience building machine learning models or developing algorithms for business application
Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience with training and deploying machine learning systems to solve large-scale optimizations