Applied Scientist, Pricing Science

Description

Amazon's Pricing Science is seeking a driven Applied Scientist to harness planet-scale multi-modal datasets and navigate a continuously evolving competitor landscape to regularly generate fresh customer-relevant prices on billions of Amazon products worldwide. We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group. The role involves measuring, refining, and launching customer-obsessed improvements to our pricing algorithms across all products listed on Amazon.

Key Job Responsibilities

• Understand and develop science to influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques
• Partner with product, engineering, and data teams within Pricing & Promotions to deploy models at Amazon scale
• Stay informed by establishing mechanisms to stay up-to-date on latest scientific advancements in machine learning, reinforcement learning, causal ML, and multi-objective optimization techniques, and identify opportunities to apply them to relevant Pricing & Promotions business problems
• Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery, successfully executing and delivering using exceptional technical machine learning expertise to incrementally move the needle on some of our hardest pricing problems

About The Team

The Pricing Discovery and Optimization team within P2 Science owns price quality, discovery, and discount optimization initiatives, including criteria for internal price matching, price discovery into search, p13N and SP, pricing bandits, and Promotion type optimization. They leverage planet-scale data on billions of Amazon and external competitor products to build advanced optimization models for pricing, elasticity estimation, product substitutability, and optimization. They ensure Amazon's prices are always competitive and error-free.

Basic Qualifications

• 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 algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• Experience in solving business problems through machine learning, data mining, and statistical algorithms

PREFERRED QUALIFICATIONS

• Experience building machine learning models or developing algorithms for business applications
• 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