Amazons Pricing & Promotions Science is seeking a driven Sr. Applied Scientist to harness planet scale multimodal datasets and navigate a continuously evolving competitor landscape in order to regularly generate fresh customerrelevant prices on billions of Amazon and Third Party Seller products worldwide.
We are looking for a talented organized and customerfocused Science lead to join our Pricing and Promotions Optimization science group with a charter to measure refine and launch customerobsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon.
This is a highopportunity highambiguity space and requires an individual with exceptional machine learning modeling expertise excellent crossfunctional collaboration skills business acumen and an entrepreneurial spirit. We are looking for a clear thinking selfstarter who is comfortable with ambiguity demonstrates strong attention to detail and has the ability to work in a fastpaced and highleverage environment.
Key job responsibilities
See the big picture. Understand and influence the long term vision for Amazons sciencebased competitive perceptionpreserving pricing techniques
Build strong collaborations. Partner with product engineering and science teams within P2 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 multiobjective 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.
About the team
The P2 Optimization team owns price quality discovery and optimization initiatives including criteria for price matching price setting internal price discovery within partner surfaces (e.g. search) pricing bandits and Promotion type optimization.
We 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. We preserve long term customer trust by ensuring Amazons prices are always competitive and error free.
5 years of building machine learning models for business application experience
PhD or Masters 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 scikitlearn Spark MLLib MxNet Tensorflow numpy scipy etc.
Experience with large scale distributed systems such as Hadoop Spark etc.
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Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150400/year in our lowest geographic market up to $260000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on jobrelated knowledge skills and experience. Amazon is a total compensation company. Dependent on the position offered equity signon 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.